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The study encompasses profiles of major companies operating in the MEA Cloud Infrastructure Services Market. Key players profiled in the report include:
AWS
Microsoft Corporation
Google
IBM
Key Points Covered in the Report: Overview of manufacturing landscape
 Outline on Consumption and Production Patterns
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Segment-wise Assessment

By the product type, the market is primarily split into
Public Cloud
Private Cloud
Hybrid Cloud

By the end-users/application, this report covers the following segments
SME
Large Organization

The key regions covered in the MEA Cloud Infrastructure Services market report are:
North America (U.S., Canada, Mexico)
South America (Cuba, Brazil, Argentina, and many others.)
Europe (Germany, U.K., France, Italy, Russia, Spain, etc.)
Asia (China, India, Russia, and many other Asian nations.)
Pacific region (Indonesia, Japan, and many other Pacific nations.)
Middle East & Africa (Saudi Arabia, South Africa, and many others.)

Segment Assessment:
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The referential data compilation evaluating regional developments and geographical guide to ascertain multiple developments across countries, localities and other global areas. This referential document assessing the market has been compiled to understand diverse market developments across specific regional pockets such as Europe, North and Latin American countries, APAC nations, as well as several countries across MEA and RoW.

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With the UK in the midst of its third lockdown of the last twelve months, the enforced shift to remote learning has been a learning curve for schools, universities and other education providers, with teachers and students now required to access course materials and administrative documents via digital means. It has shown just how essential digital solutions are to creating a successful learning environment online.

Additionally, continued advances in the education sector mean that it’s an industry generating huge troves of data. The data produced is vast and varied: from research data to digitised library assets, student and faculty information, and video assets. As institutions continue to operate remotely, the scale of data is increasing rapidly.

The changing landscape of education requires that students and staff become more data literate. It also means that IT departments need to develop a comprehensive data management strategy for planning and implementing increasingly data-intensive education systems – one that’s efficient, compliant, and doesn’t cost the earth.

Keep your data secure
As data proliferates, one of the biggest demands for educational institutions is how to store their data cheaply and in a way that is easily and quickly accessible from everywhere.

Unless checked, organisations can easily lose control of where data is stored and how it is used. This can especially become a problem where IT security policy is concerned, so it is worth enforcing system wide protocols such as two-factor authentication to protect data against potential breaches from external threats or insiders.

A backup and disaster recovery solution is also essential. Here I’d advise adopting the ‘3-2-1 rule’ in association with cloud backups: keeping three copies of your data on at least two different formats with one copy offsite is also a good strategy. Should something happen to the primary copies of your data, your copies in the cloud will remain unaffected and will enable you to get back up to speed with little business impact. Moreover, a good storage provider should offer immutable buckets – a means of storage which renders contents fixed and unchangeable according to the minimum data immutability standards, to guard against accidental alterations or deletions.

Keep costs down
Given the growth in digital data, educational institutions are consuming far more storage space than they did a few years ago. In addition to student records and digital coursework, it’s increasingly common for institutions to need to store video lectures and other supplementary materials. Some universities have even created entire digital libraries and archives from offline resources that are accessible online to students and staff to bolster their offerings to students, teachers and faculty members.

When dealing with the petabytes of data generated by educational institutions, primary, on-prem storage can quickly eat up the IT budget as you keep needing to store more data. Cloud storage takes advantage of economies of scale, providing the ability to dynamically scale resources up or down as required, and is therefore a more cost-effective option for universities looking to store and access their data in a way that’s fast and easy to access.

However, that’s not to say all cloud storage solutions are equally economical. Many first-generation cloud providers, like Amazon or Google, don’t just charge customers based on the amount of data stored, but charge extra for accessing the data. This could quickly prove problematic for universities, if faculty members need frequent access to files. Fortunately, not all storage providers work on these models – a number of second generation services operate on all-you-can eat data models without any surprises in the form of egress fees.

IT decision makers should act as their own managed service providers (MSPs) – offering support for multiple cloud services, rather than being the gatekeepers of one provider. Rather than relying on outside MSPs to help them set up networks, provision compute resources, recommend and implement software, and so forth, education IT departments should play an active role in considering staff and student needs internally to help them choose the right solutions and avoid incompatible and fragmented services.

Embrace the hybrid cloud
Adopting a hybrid cloud approach can give you the ability to flexibly scale up on existing on-premises legacy systems. This approach enables you to keep on hand a larger amount of storage whilst relieving the stress on IT teams by moving some applications into public clouds.

With a hybrid cloud solution, IT decision makers can optimise infrastructure to meet a plethora of needs and diverse workload requirements. For example, there are applications that require rapid deployment and/or rapid scaling which are a better fit for public cloud. Other systems such as HR can be moved to a software as-a-service (SaaS) model to free up storage and on-prem capacities. With hybrid cloud, educational systems can dynamically shift workloads between on and off-premises environments based on specific performance, regulatory needs or for specific periods of time.

A hybrid cloud approach is also useful from a security perspective and can help combat the “shadow IT” problem. Key systems containing sensitive student and school data can remain in-house and behind an educational organisation’s firewall to safeguard student privacy and meet compliance regulations, whilst employees can be empowered to use public clouds for different use cases as appropriate.

There’s no doubt that the pandemic has showcased the importance of transitioning an organisation’s infrastructure to a cloud-centric approach, something that’s been recommended as far back as 2019 by the UK’s Department for Education. Today, educators can see huge benefits from adopting next-generation cloud technologies, and the transition needn’t be costly nor complex – and to protect the future of remote learning and collaboration, it’s no longer a want, but a need.

Overview: An Open, Provider-Agnostic Hybrid Cloud Stack

Red Hat — acquired by IBM in 2019 but operating with significant autonomy — used its 2020 Summit to articulate a sharp and detailed strategy for leading the hybrid cloud market. The company’s thesis is straightforward: customers need an infrastructure stack that is open-source, consistent across environments, and invisible to application developers. Rather than locking customers into any single provider’s tooling, Red Hat bets that a vendor-neutral, community-driven platform will win enterprise adoption at scale.

Red Hat Summit 2020 was held as a virtual event due to the COVID-19 pandemic. Keynotes and panel discussions featured newly appointed CEO Paul Cormier alongside a wide roster of product and engineering leaders who walked through each layer of the company’s cloud strategy.

Layer 1: Red Hat Enterprise Linux as the Foundation

Red Hat Enterprise Linux (RHEL) remains the company’s flagship product and the base on which everything else is built. At Summit 2020, Red Hat made RHEL 8.2 generally available, adding enhanced monitoring capabilities and improved container support.

CEO Paul Cormier framed RHEL’s historical significance during his keynote panel: “Linux started an innovation cycle that continues in full force today” — through virtualization, cloud computing, containers, Kubernetes, and now edge computing.

Stephanie Chiras, Vice President and General Manager for RHEL, reinforced that point: “Linux provides the foundation upon which all this innovation is based.”

Gunnar Hellekson, Senior Director of Product Management and Strategy, noted that RHEL’s defining market advantage is operational consistency — the ability to deploy workloads reliably across on-premises data centers, multiple public clouds, and edge environments. That consistency, he argued, compounds in value as it propagates up through higher-order products in the Red Hat portfolio.

Tim Cramer, Vice President for Software Engineering, acknowledged a productive internal tension: OpenShift’s rapid growth is placing significant pressure on the RHEL roadmap, driving the operating system to evolve faster to support container-native and cloud-native workloads.

Layer 2: OpenShift as the Cloud-Era Platform

While RHEL built Red Hat’s reputation, OpenShift — the company’s enterprise Kubernetes platform — is the product Red Hat is betting on for long-term cloud dominance. At Summit 2020, Red Hat announced OpenShift 4.4 with a set of capabilities targeted at both near-term enterprise needs and emerging workloads.

Key OpenShift 4.4 Capabilities

• Kubernetes 1.17 implementation — bringing the platform in line with the upstream project.
• Advanced Cluster Management — simplifying administration of large, distributed Kubernetes deployments.
• Policy-based management — enabling dynamic scaling in environments that must respond rapidly to changing demand.
• OpenShift Virtualization — described by Matt Hicks, Executive Vice President for Products and Technologies, as the release’s “revolution.”

OpenShift Virtualization integrates the KubeVirt open-source project, allowing virtual machines (VMs) to be imported and run natively inside OpenShift alongside Kubernetes-managed containers. This removes a long-standing architectural divide: teams no longer have to choose between their existing VM-based infrastructure and a modern container platform. Hicks argued this bridges legacy infrastructure and cloud-native operations, reduces licensing overhead, and creates new pathways for industries — such as telecommunications preparing 5G deployments — that cannot immediately abandon their existing VM estates.

Layer 3: Open Hybrid Cloud as the Competitive Differentiator

Red Hat CTO Chris Wright described the hybrid cloud landscape as having expanded far beyond simple on-premises/public-cloud combinations. Infrastructure now spans data centers, multiple public clouds, and distributed edge nodes. Red Hat’s open-source portfolio — built on years of Linux, virtualization, and container development — positions the company to serve all of those environments with a consistent platform.

Wright also pointed to the next frontier: reducing the cognitive overhead on developers working in hybrid environments. New paradigms such as serverless computing and event-driven architectures are central to that goal, allowing teams to focus on application logic rather than infrastructure provisioning and management.

CEO Cormier was direct about what differentiates Red Hat: “This is what open, hybrid cloud is.” Kubernetes-centered innovation, he argued, has created the industry’s largest hybrid cloud ecosystem and placed Red Hat at its center.

Layer 4: Multi-Cloud as the Natural Evolution

Joe Fernandes, Vice President and General Manager for Core Cloud Platforms, noted that most Red Hat customers have already adopted hybrid infrastructure or are actively moving toward it. But the journey does not stop there — customers “then tend to move to a multi-cloud strategy.”

Mike Evans, Vice President for Technical Business Development, described the current environment as an “amalgamation of hybrid cloud, multi-cloud, and cloud-to-cloud” activity — with the expectation that complexity will increase rather than decrease.

Red Hat has cultivated deep partnerships with Amazon Web Services, Microsoft Azure, Google Cloud, and parent company IBM to support this multi-cloud reality. Evans also highlighted a notable industry trend: hyperscalers are themselves beginning to offer services that run on competitors’ clouds — Google’s Anthos and Microsoft’s Azure Stack being prominent examples. In effect, the large cloud providers are building their own cross-cloud hybrid strategies, which Evans argued validates Red Hat’s long-standing multi-cloud position.

Layer 5: Edge Computing as an Extension of Hybrid Cloud

CTO Wright acknowledged that “edge” is a broad and sometimes loosely defined concept, but framed it simply: putting computing closer to users and the data they need. CEO Cormier echoed that definition.

Joe Fitzgerald, Vice President and General Manager of Red Hat’s Management Business Unit, noted that edge workloads span multiple tiers — from cloud-adjacent aggregation nodes down to devices at the network perimeter. Red Hat’s focus, he said, is on the customer’s physical premises: the servers, gateways, and other hardware that organizations place at the edge of their networks.

The telecommunications industry is a particularly important edge use case. Verizon’s 5G buildout was highlighted during Cormier’s Summit keynote as an example of the scale and complexity involved. Fitzgerald described the technical challenge: managing a vast, geographically distributed computing infrastructure with enough speed and consistency to deliver next-generation network services.

Red Hat’s response involves extending real-time capabilities into the RHEL portfolio and OpenShift, enabling policy-based, automated management of distributed edge systems from a single consistent platform. As Fitzgerald summarized: “It’s all about speed and automation.”

The Kubernetes Maturity Curve

Five years after Google open-sourced Kubernetes, Brian Gracely, Senior Director for Product Strategy for OpenShift, argued the technology is entering a third generation of enterprise use cases. The first wave required deeply technical operations teams willing to navigate immature tooling without vendor support. The second wave brought broader enterprise adoption as the tooling stabilized. The third wave — now underway — is about making Kubernetes accessible to developers who are not infrastructure specialists at all.

Clayton Coleman, a Red Hat Solutions Architect for Containerized Application Infrastructure, framed the new challenges clearly: AI, machine learning, big data, and edge computing are the next frontiers. These workloads require domain experts — data scientists, ML engineers — who need to run complex infrastructure without becoming Kubernetes administrators.

The design goal for Red Hat’s Kubernetes platform, Coleman said, is maximum empathy for administrators and developers alike: clear guardrails, automated operators, and an ecosystem that puts teams on a well-defined path to production success. “We try to make it so Kubernetes is the easiest place to build and run applications,” he said.

VMs, Containers, and the VMware Comparison

Ashesh Badani, Senior Vice President for Cloud Platforms, addressed a question Red Hat executives frequently face: how does the company’s container strategy compare to VMware’s? Badani drew several distinctions:

• Kubernetes experience: Red Hat has shipped Kubernetes-based platforms for five years with a large installed customer base; VMware’s Kubernetes efforts, Badani argued, are comparatively nascent.
• Standards alignment: Red Hat’s platform is built on a Cloud Native Computing Foundation (CNCF) standardized Kubernetes stack. Badani characterized VMware as still deciding among multiple Kubernetes stacks, some of which are proprietary.
• Hybrid cloud architecture: Red Hat’s approach uses a consistent open stack across environments; VMware’s, Badani said, incorporates proprietary infrastructure per public cloud.
• VM and container convergence: Red Hat takes a cloud-native approach via KubeVirt and OpenShift Virtualization; Badani characterized the alternative as wrapping proprietary APIs around an existing virtualization platform.
• Open-source community: Red Hat has participated in open-source communities for over two decades, Badani noted, calling that depth of community engagement a genuine differentiator.

Note: These comparisons represent Red Hat’s own characterization of the competitive landscape as presented at its Summit. VMware’s perspective is not represented here.

Developer-First Infrastructure

A consistent theme across every layer of Red Hat’s strategy is that infrastructure should be invisible to the developers who use it. Fernandes stated it plainly: “Developers should be shielded from where those applications are running.”

Red Hat’s model — build once, package as a container, deploy anywhere — enables application teams to write code without caring whether it ultimately runs on-premises, on AWS, on Azure, or on Google Cloud. Administrators handle placement decisions; developers focus on the application.

This abstraction is especially important for specialized workloads. CTO Wright described how machine learning environments typically require hardware acceleration at the OS level, with OpenShift providing the abstraction layer that makes those accelerated resources accessible to data scientists without requiring them to understand the underlying infrastructure.

Cloud Provider Partnerships and Joint Engineering

Mike Farris, Vice President for Strategy, Products and Technologies, noted that Red Hat has focused not only on maturing its technology but on deepening its cloud provider partnerships. Even though the major hyperscalers offer their own container services, Evans said they recognize OpenShift as a platform their joint enterprise customers specifically request — particularly for migrating mission-critical applications or developing cloud-native workloads.

Enterprise customers are pressing cloud providers to offer jointly managed and, in some cases, jointly engineered solutions based on OpenShift — a dynamic already visible in Azure Red Hat OpenShift, a service co-engineered and co-supported by Microsoft and Red Hat. Farris indicated that similar deeper partnerships with other cloud providers were expected to follow.

COVID-19 Impact and Customer Support Measures

Cormier noted that the pandemic had limited operational impact on Red Hat’s engineering and product development, partly because a significant portion of the company’s engineers and developers already worked remotely before the crisis. “We’re going full force on the road map and product line we planned from the beginning,” he said.

Go-to-market activities requiring direct customer engagement proved more challenging to adapt. Red Hat responded with several customer-facing measures:

• Extended product life cycles — giving customers more time before being required to upgrade, reducing upgrade pressure during an already disruptive period.
• Reduced Technical Account Management costs — cutting prices for new customers by half.
• Free training programs — offered to furloughed workers and job seekers looking to build cloud and open-source skills.

Summary: The Five Pillars of Red Hat’s Hybrid Cloud Strategy

1. RHEL as the universal foundation — operational consistency across every environment.
2. OpenShift as the cloud-era platform — Kubernetes plus VM convergence, abstracted for developers.
3. Open hybrid cloud — vendor-neutral, standards-based, bridging data center and cloud.
4. Multi-cloud by design — deep partnerships with all major hyperscalers; provider-agnostic by architecture.
5. Edge as hybrid cloud’s frontier — automated, policy-driven management of distributed infrastructure at scale.

Red Hat’s strategy depends on a coherent bet: that enterprise customers will prefer an open, consistent platform that works everywhere over any single provider’s proprietary stack. Whether the market ultimately rewards that approach will depend on execution — and on whether the open-source community continues to rally around the technologies Red Hat is building on.

Kubernetes Becomes the Default Target for Database Operators

Whether proprietary or open source, NoSQL or relational, database vendors are converging on Kubernetes as the deployment and operations platform of choice. The release of Couchbase’s Autonomous Operator 2.0 is the latest example of a major database vendor leaning into container orchestration — not as an afterthought, but as a first-class deployment strategy.

Couchbase is a document-oriented NoSQL database used by large enterprises including Amadeus, American Express, Cisco, and eBay. Its Autonomous Operator for Kubernetes 2.0 is engineered to let developers and database administrators deploy and manage Couchbase clusters across any cloud environment with minimal manual intervention.

What the Autonomous Operator 2.0 Actually Does

At its core, the Autonomous Operator extends the Kubernetes API via a Custom Resource Definition (CRD) that acts as a declarative blueprint for a Couchbase cluster. Perry Krug, Couchbase Director of Customer Success, described the capability:

“It allows you, with just a single click, to deploy a Couchbase cluster into Kubernetes. In some cases, you can deploy many nodes that will automatically cluster together having the same configuration.”

A key design goal is portability. The same CRD template works across:

• Vanilla upstream Kubernetes
• Red Hat OpenShift
• Amazon Elastic Kubernetes Service (EKS)
• Microsoft Azure Kubernetes Service (AKS)
• Google Kubernetes Engine (GKE)

This means a team can write a cluster definition once and deploy it consistently across on-premises, hybrid, or multi-cloud environments without rewriting configuration for each target platform — a meaningful reduction in operational overhead for teams managing geographically distributed workloads.

Reported Operational Gains

Couchbase has claimed that users of the Autonomous Operator reported a 95% reduction in operational complexity and cost when deploying the database in the cloud. The operator is also positioned as a tool to simplify hybrid cloud installations and reduce dependency on any single cloud vendor’s proprietary managed database services.

It is worth noting that this figure comes from Couchbase’s own customer reporting and has not been independently verified. Teams evaluating the operator should benchmark against their own operational baseline.

How Kubernetes Operators Change Database Operations

The Kubernetes Operator pattern encodes domain-specific operational knowledge — things a skilled DBA would do manually — into a controller that runs inside the cluster. For stateful workloads like databases, this changes the operational model in several concrete ways:

• Automated provisioning: Nodes are created, joined to the cluster, and configured to a defined spec without manual intervention.
• Consistent replication: The same declarative configuration can be applied to development, staging, and production environments.
• Self-healing: When a node fails, the operator can detect the failure and initiate recovery automatically, rather than waiting for a DBA to respond.
• Continuous reconciliation: Kubernetes monitors the actual cluster state against the desired state defined in the CRD and acts to correct drift.

Percona XtraDB Cluster 8.0: The Open-Source Parallel

Couchbase is not alone in this direction. Around the same time, Percona — the open-source database services company — released XtraDB Cluster 8.0, which includes a Kubernetes operator for MySQL-compatible clusters. Percona’s product marketing manager Rick Golba explained the operational model:

“It creates the nodes and it creates the connectivity between the nodes. It sets it all up to your standards and in a way that is very consistent and easily replicated to other environments. Similarly, when you’re running the environment, Kubernetes is going to be monitoring the environment.”

The node failure recovery use case is particularly significant for high-availability database clusters. Golba elaborated:

“In the past, if you had a node that failed in a three-node cluster, your environment would stay up and running and you could do all of the recovery from that, but that was a manual process. Now with Kubernetes, we have the capability to automate that whole entire process.”

The Broader Trend: Database Operators as Operational Infrastructure

The convergence of database vendors on Kubernetes operators reflects a broader shift: infrastructure teams increasingly expect stateful workloads to be managed through the same declarative, GitOps-compatible tooling as the rest of their application stack. Native Kubernetes integration means database lifecycle management — including provisioning, scaling, upgrades, and failure recovery — can be expressed as code, reviewed, and version-controlled alongside application configuration.

For DevOps and platform engineering teams, the practical implication is that the operational gap between managing stateless microservices and stateful databases is narrowing. Whether you are running Couchbase for document workloads or MySQL for transactional data, the Kubernetes operator model provides a consistent abstraction for cluster lifecycle management across cloud environments.

Key Takeaways

• Couchbase Autonomous Operator 2.0 enables declarative, single-CRD deployment of Couchbase clusters across vanilla Kubernetes, OpenShift, EKS, AKS, and GKE.
• The operator targets reduced manual DBA intervention and supports hybrid and multi-cloud deployments without configuration rewrites per platform.
• Couchbase reports a 95% reduction in operational complexity — a vendor-supplied figure that teams should validate against their own environments.
• Percona’s XtraDB Cluster 8.0 brings the same operator-based automation model to open-source MySQL workloads.
• The Kubernetes Operator pattern is becoming an expected standard for production database deployment, not a differentiating feature.

What Is a Hybrid Cloud Management Platform?

A hybrid cloud management platform (HCMP) is software that gives organizations a unified control plane for resources spread across on-premises infrastructure, private clouds, and one or more public cloud providers. Rather than logging into separate consoles for AWS, Azure, VMware, or bare-metal systems, operations and DevOps teams use a single interface to provision workloads, enforce policies, monitor costs, and automate governance across all environments.

The core value proposition is consistency: the same security policies, tagging standards, cost-allocation rules, and automation workflows apply regardless of where a workload runs. This becomes increasingly important as enterprises operate fleets of hundreds or thousands of virtual machines, containers, and serverless functions across multiple clouds simultaneously.

Key Capabilities to Evaluate

When assessing any HCMP, DevOps and platform engineering teams typically evaluate the following capability areas:

• Self-service provisioning: Developer portals or service catalogs that let teams spin up approved infrastructure without raising a ticket.
• Policy and compliance enforcement: Guard-rails that prevent misconfigured or non-compliant resources from being created, or that remediate them automatically.
• Cost visibility and chargeback: Tagging-based attribution of cloud spend to teams, projects, or business units, with budget alerting.
• Workload migration and placement: Tools to move VMs or containers between environments based on cost, latency, or compliance requirements.
• Monitoring and observability integration: Native metrics dashboards or integrations with tools such as Datadog, Prometheus, or Grafana.
• ITSM and workflow integration: Connections to service management platforms (e.g., ServiceNow) for change control, incident management, and approval workflows.

Notable Vendors in the Hybrid Cloud Management Space

The 2020 research summary highlighted the following platforms as active competitors in the HCMP market. Brief descriptions reflect publicly available positioning at the time; the space evolves quickly and some product names or company structures may have changed since publication.

• Embotics Commander (now part of VMware): A self-service cloud management and IT governance platform aimed at automating VM lifecycle management across hybrid environments.
• CloudController: A multi-cloud management product focused on simplifying provisioning and billing across cloud providers.
• Tanaza: Primarily a cloud-based Wi-Fi network management platform; its inclusion in a hybrid cloud management report may reflect a broader definition of cloud-managed infrastructure.
• FUJITSU: The Japanese IT conglomerate offers hybrid cloud solutions targeted at enterprise and government customers, often bundled with managed services.
• Flexiant (now Flexiwan): Originally offered a cloud orchestration platform for service providers; the company subsequently pivoted toward SD-WAN.
• RackWare: Specializes in cloud migration, disaster recovery, and workload mobility across on-premises and public cloud environments.
• ServiceNow: Best known as an ITSM platform, ServiceNow’s cloud management module integrates resource provisioning and governance with service catalog and change management workflows.
• ManageEngine (Zoho): Provides a broad suite of IT management tools, including cloud cost management and infrastructure monitoring products.
• Datadog: Primarily a monitoring and observability platform; it is increasingly used as a component within hybrid cloud management stacks for metrics, tracing, and log management.

The competitive landscape is not limited to these vendors. Platforms such as HashiCorp Terraform, Red Hat Ansible Automation Platform, VMware Aria (formerly vRealize), and Microsoft Azure Arc also address significant portions of the hybrid cloud management use case.

Market Segmentation: How Analysts Classify the Space

Commercial research reports on this market typically segment by several dimensions. Understanding these segments helps DevOps teams map vendors to their specific requirements:

By Deployment Model

• SaaS-delivered: The management platform itself runs in the vendor’s cloud. Lower operational overhead but raises data-sovereignty questions for highly regulated industries.
• On-premises or self-hosted: The platform runs inside the customer’s data center or private cloud. Preferred where compliance mandates data residency.
• Hybrid of the above: A control plane hosted by the vendor with agents or collectors running on-premises.

By Organization Size

• Enterprise: Large organizations typically require role-based access control, audit trails, multi-tenancy, and integration with existing ITSM and identity systems.
• SMB / Mid-market: Smaller teams often prioritize ease of setup and predictable SaaS pricing over deep customization.

By Industry Vertical

Financial services, healthcare, and government verticals typically drive demand for stricter compliance controls, audit logging, and the ability to keep sensitive workloads on-premises while using public cloud for less sensitive applications. Retail and media tend to prioritize elastic scaling and cost optimization features.

By Geography

The research report segments the market across North America, Europe, Asia-Pacific, South America, the Middle East, and Africa. North America and Western Europe have historically been the largest adopters of enterprise cloud management tooling, while Asia-Pacific has shown strong growth rates driven by digital transformation initiatives in manufacturing and financial services.

COVID-19’s Effect on Hybrid Cloud Adoption (2020 Context)

The original report was published in April 2020, during the early months of the COVID-19 pandemic. The pandemic’s near-term effect on hybrid cloud management was mixed:

• Accelerated cloud migration: Organizations that had deferred cloud migration projects were forced to act quickly to support remote workforces, increasing demand for cloud management tooling.
• Budget pressure: Simultaneously, many IT budgets were frozen or cut, pushing teams toward cost optimization features within management platforms rather than net-new purchases.
• Supply chain disruption: Data center hardware procurement timelines lengthened, making the elasticity of public cloud more attractive and reinforcing the hybrid model.
• Security and compliance scrutiny: Rapid cloud adoption without adequate governance raised concerns, increasing interest in platforms that could enforce policy at scale.

Factors Driving and Restraining Adoption

Drivers

• Growing multi-cloud adoption creating management complexity that single-cloud-native tools cannot address.
• Regulatory requirements mandating data residency or audit trails that necessitate a hybrid rather than fully public cloud architecture.
• Cost optimization pressure as cloud bills grow and finance teams demand accountability for cloud spend.
• Platform engineering as a discipline gaining organizational recognition, creating internal demand for developer self-service tooling.

Restraints

• Integration complexity: connecting a management platform to legacy on-premises systems, existing ITSM tools, and multiple cloud provider APIs is non-trivial.
• Skills gap: operating an HCMP effectively requires expertise in cloud architecture, networking, security, and automation—skills that are in short supply.
• Vendor lock-in risk: some platforms use proprietary abstractions that make it difficult to migrate away.
• Total cost of ownership: licensing, implementation, and ongoing administration costs can be significant, especially for self-hosted deployments.

Analytical Frameworks Used in Market Research

The original report references Porter’s Five Forces as a framework for competitive analysis. Applied to the HCMP market, this means examining:

• Threat of new entrants: Moderate. Cloud-native startups can build new management layers relatively quickly, but enterprise sales cycles and integration requirements create significant barriers.
• Bargaining power of buyers: Increasing. Large enterprises can negotiate pricing and often have the leverage to build internal tooling (e.g., using Terraform and Backstage) as an alternative.
• Bargaining power of suppliers: High for hyperscalers (AWS, Azure, GCP), whose API changes and pricing decisions directly affect management platform vendors.
• Threat of substitutes: High. Cloud-native management consoles, open-source tools, and internal platform engineering teams can substitute for commercial HCMPs in some scenarios.
• Competitive rivalry: Intense. The market includes large established vendors (ServiceNow, Fujitsu), specialized independents, and hyperscaler-native tools all competing for the same budget.

What DevOps Teams Should Take Away

Market research reports are primarily written for investors and executive decision-makers, but the underlying data is useful context for DevOps and platform engineering practitioners evaluating tooling:

• The HCMP market is competitive and fragmented. No single vendor dominates across all use cases, which means shortlisting should start from your specific requirements (compliance, scale, existing toolchain integrations) rather than from analyst rankings alone.
• Vendor consolidation is ongoing. Several platforms mentioned in 2020 reports have since been acquired or pivoted. Validate current product status and roadmap before committing to a platform.
• Open-source alternatives deserve evaluation. Tools like Crossplane, HashiCorp Terraform with Atlantis, and Backstage as an internal developer portal can cover significant portions of the HCMP capability map without commercial licensing costs.
• Total cost of ownership extends beyond licensing. Factor in implementation effort, training, and ongoing operations when comparing commercial platforms against internal builds.

Source Attribution

This article is based on a market research summary originally published at bestdevops.com (post ID 12062, April 2020), which cited amitnetserver.com as the source for a commercial market research report attributed to Reports And Markets / Algoro Research Consultants Pvt. Ltd. The original full report was a paid commercial document; this article does not reproduce proprietary data or forecasts from that report.

1. Artificial Intelligence Becomes More Deeply Integrated

AI capabilities embedded in cloud platforms matured significantly in 2020. Cloud providers began offering managed AI and machine learning services that allowed organizations to automate complex processes without building infrastructure from scratch.

Core AI disciplines — including machine learning, computer vision, and intelligent process automation — became accessible via cloud APIs. This lowered the barrier to entry for enterprises wanting to derive insights from large datasets, scale predictive models, and streamline repetitive workflows.

2. Kubernetes Becomes the Container Orchestration Standard

Kubernetes, the open-source container orchestration platform originally developed at Google, cemented its position as the de facto standard for deploying and managing containerized workloads in 2020. Its adoption extended well beyond early adopters into mainstream enterprise environments.

According to a survey referenced by RedMonk, Kubernetes appeared in some form in approximately 54% of Fortune 100 companies. Major cloud providers — including AWS (EKS), Google Cloud (GKE), and Microsoft Azure (AKS) — offer fully managed Kubernetes services, further accelerating adoption by reducing operational overhead.

Key reasons for Kubernetes dominance include:

• Automated deployment and scaling: Kubernetes handles rolling updates, self-healing, and horizontal pod autoscaling out of the box.
• Portability: Workloads can move between on-premises and cloud environments with minimal reconfiguration.
• Rich ecosystem: Tools like Helm, Istio, and Prometheus integrate tightly with Kubernetes to cover packaging, service mesh, and observability needs.

3. Serverless Computing Gains Traction

Serverless computing does not mean the absence of servers — it means that the cloud provider fully manages the underlying server infrastructure, and developers deploy individual functions or event-driven code without provisioning or maintaining servers directly.

The core operational advantages of serverless architectures include:

• Pay-per-execution pricing: Customers are billed only when code runs, not for idle capacity.
• Automatic scaling: The platform scales functions up or down in response to demand with no manual intervention.
• Reduced operational burden: Teams focus on writing business logic rather than managing runtime environments or patching OS dependencies.

AWS Lambda, Google Cloud Functions, and Azure Functions are the dominant serverless offerings. While real-world adoption was still maturing in 2020, serverless was widely regarded as a transformative shift in how cloud-native applications are designed and operated.

4. Vendor Consolidation: Doing More with Fewer Providers

A growing number of organizations moved to consolidate their cloud and SaaS vendor portfolios in 2020. Managing a fragmented toolchain — with separate vendors for storage, compute, security, monitoring, and collaboration — creates integration overhead, inconsistent support experiences, and hidden costs.

The cloud made consolidation more practical. Major providers expanded their service catalogs to cover more use cases under a single contract and billing structure. Organizations benefited from:

• Simplified procurement and licensing negotiations.
• Better native integration between services from the same provider.
• Reduced tool sprawl and lower total cost of ownership.

This does not mean abandoning multi-cloud strategies entirely — many organizations maintained relationships with two or three strategic providers — but the era of accumulating dozens of niche point solutions was under pressure.

5. Hybrid Cloud Architectures Mature

The hybrid cloud model — combining private cloud or on-premises infrastructure with public cloud services — gained significant momentum as enterprises sought greater flexibility without abandoning existing investments.

In a hybrid cloud model:

• Sensitive or regulated workloads (financial data, patient records, intellectual property) remain on private infrastructure where data residency and compliance requirements can be tightly controlled.
• Variable or burst workloads are offloaded to the public cloud, providing elastic capacity without over-provisioning on-premises hardware.
• Development and testing environments can be spun up and torn down in the public cloud rapidly, reducing costs and cycle times.

Platforms such as AWS Outposts, Azure Arc, and Google Anthos emerged to help organizations extend cloud-native tooling and management planes to on-premises and edge environments, making hybrid architectures more cohesive and easier to operate.

6. Containers Drive Consistent Software Delivery

Containers continued to mature as the standard packaging format for cloud-native applications in 2020. By bundling an application with its runtime dependencies into a portable image, containers eliminate the classic “it works on my machine” problem across development, staging, and production environments.

Cloud-managed container registries and orchestration platforms lowered the operational cost of running containerized workloads at scale. As the ecosystem stabilized around Kubernetes as the orchestration layer, more organizations moved production workloads into containers, enabling faster release cycles and more reliable deployments.

The widespread availability of container-as-a-service offerings from major cloud providers also allowed smaller engineering teams to adopt containers without needing deep infrastructure expertise.

Key Takeaways

The cloud computing landscape in 2020 was characterized by maturation rather than novelty. AI services became more practical and accessible, Kubernetes solidified its dominance in orchestration, serverless changed the economics of compute, hybrid cloud provided enterprises a pragmatic path forward, and containers became the standard unit of software delivery. Organizations that understood and acted on these trends were better positioned to build resilient, cost-efficient, and scalable infrastructure.

Proliferation of hybrid and multi-cloud architectures has resulted in rapid and remarkable technological advances. But it has also introduced monumental, multi-faceted complexities into IT infrastructure management, including:

Managing multiple on-premises, private and public cloud resources
Complicated and lengthy provisioning processes for resource deployment
Continually evolving challenges maintaining visibility into multi-cloud ecosystems
Ongoing concerns around governance, cost control and security
In the face of these challenges, enterprise IT must enable innovation and digital transformation. Whether through self-service and intelligent automation, or through building a base of continuous insights driven by AI, machine learning, and deep learning, adopting these new paradigms has never been more critical. Simultaneously, IT needs to stay operationally vigilant regarding visibility, governance and cost control as organizations accelerate their hybrid cloud growth.

Unfortunately, many IT leaders are struggling to successfully adapt to these challenges. According to ESG’s 2019 Technology Spending Intention Survey, most executives view IT as a drag on — not a driver of — innovation. Specifically, a mere 6 percent consider IT a competitive differentiator, and a full quarter of executives actually see IT as a business inhibitor. In a rapidly changing world of cloud, there has to be a better way.

By adopting these five core principles of IT infrastructure management, IT leaders can become strategic business drivers and better meet end users where they are:

1. Self-service

To prevent bottlenecks, IT must institute a self-service delivery model enabling developers to access resources when they need them. When end users are able to quickly and easily access the resources they need – storage, compute, networking and more – without having to go through IT, organizations can reach a whole new level of agility and productivity.

2. Intelligent automation

Self-service depends on automation – intelligent automation. It must ensure the orchestrated delivery of IT resources — whether that’s infrastructure or advanced services such as IoT, blockchain and developer tools — fit the end user’s role and responsibilities as well as the needs of the business. With intelligent automation, IT teams can build security and cost guardrails into provisioning those resources, covering everything from access privileges to how long provisioned resources remain available in the cloud. The result? Better governance and cost control.

3. Comprehensive visibility

Business and IT leaders don’t need visibility into some of their infrastructure; they need visibility into all of their infrastructure — whether that’s virtual machines on-prem, to private cloud networks, or multi-tier, load balanced applications in AWS, Azure, GCP and others. And they need this visibility consolidated into a single view, the proverbial “single pane of glass.” Unified visibility enables modern IT leaders to always know where and how resources have been deployed, monitoring all usage from a single platform.

4. Modular extensibility

To swiftly adopt new technologies and leverage existing IT investments, modern IT leaders embrace modular, extensible frameworks. You need such frameworks — for example, modular plug-in in the form of scripts, web hooks and email notifications – to support a rapidly evolving array of development tools, paradigms and priorities. For example, the world of hybrid cloud can mean using a mashup of existing tools, whether that’s Terraform for provisioning, Ansible for configuration, Veeam for cloud migration and many more. IT teams need to go beyond the API and leverage extensibility that makes plug-and-play painless. The result? Less vendor-locking and the ability to build your hybrid cloud, your way.

5. Continuous insight

Unified visibility into IT resource deployment and usage depends on the capture of data across workloads, clouds and teams. This allows IT teams to quickly identify areas of risk, sprawl and inefficiency. More impactfully, it also enables IT to pursue data-driven resource planning. With increased accuracy in rate estimation, up-to-date views of inventory and predictive provisioning based on recognized patterns of usage, IT can proactively anticipate the needs of the business and application development teams, becoming the innovation enabler it should be.

Real-world examples: The Home Depot

Situation

The Home Depot had its sights set on continuous integration and continuous delivery (CI/CD), but getting a machine provisioned took anywhere from several days to a week, causing major bottlenecks in business productivity. Also, the company lacked an effective way to track and manage resources, particularly in its hybrid cloud environment.

Solution

The company developed a self-service model, providing developers with front-facing infrastructure services. The IT team now allows thousands of users to provision cloud-based resources without submitting a ticket. What once took one to two weeks now takes 20 minutes.

Furthermore, by implementing a more flexible framework built on modular extensibility for orchestration, Home Depot made integration with existing infrastructure easy. This extensibility supports Home Depot’s evolving IT strategy and facilitates the adoption and integration of Kubernetes, Docker, Terraform and other emerging technologies.

InterContinental Hotels Group (IHG)

Situation

IHG owns over 5,000 hotels and utilizes four different public clouds. They wanted the flexibility of multiple public clouds in order to use the platform that provides the optimal environment for each workload, as well as to protect the organization from price increases and instabilities from any one public cloud. As a result, administrators needed to understand and use four different interfaces to locate, manage and provision compute resources.

Solution

IHG moved all managed resources from VMware vRA to a cloud management platform that provided a single user interface and API, through which they could deploy complex apps to VMware and any of the four public clouds they used. This enabled them to manage these apps and their servers over the course of their lifecycle, allowing for business policy automation as well as adoption of best practices around server deployment and management.

IHG now also uses comprehensive visibility and intelligent automation to control VM sprawl and protect against unexpectedly large bills from vendors. Through the use of expiration dates for servers, configurable expiration behavior, visibility into all VMs across all technologies, quotas, cost tracking and easy de-provisioning of servers and services, IHG ensures that organizations only pay for what they need and use.

The 5 principles and your organization

To tame enterprise IT infrastructure complexity, IT leaders must adopt a flexible and extensible model for IT infrastructure management that relieves pressure on IT while freeing both the enterprise and developer teams to take advantage of emerging technologies. By adopting the five core principles of IT infrastructure management, your organization can improve time-to-value for your hybrid cloud and multi-cloud management strategy.

Why SMBs Keep Choosing Microsoft Azure

Building the right technology stack used to mean negotiating contracts with multiple vendors, each responsible for a narrow slice of the overall solution. Microsoft Azure, combined with Microsoft 365 (formerly Office 365), reduces that complexity by providing a broadly integrated ecosystem: identity management through Azure Active Directory, productivity applications, storage, compute, and developer tooling all sit under the same vendor relationship and billing umbrella.

For smaller organizations with lean IT teams, the practical benefits of this consolidation include:

• Fewer integration points to maintain: Services within the Microsoft ecosystem are designed to work together out of the box, reducing the custom glue code and middleware that multi-vendor stacks often require.
• Simplified licensing and support: A single vendor relationship means a single support channel for a large portion of the stack.
• Familiar tooling for staff: Teams already using Microsoft 365 face a shorter learning curve when extending into Azure compute or storage services.
• Predictable cost modelling: Pay-as-you-go pricing, reserved instances, and Azure Hybrid Benefit (for organizations with existing Windows Server or SQL Server licences) give finance teams more levers to control spend.

Security and Compliance in the Azure Ecosystem

Security is a recurring concern for SMBs that handle sensitive customer data, financial records, or health information. Azure’s security capabilities span several layers:

• Azure Active Directory (Azure AD): Centralised identity and access management with support for multi-factor authentication and conditional access policies.
• Microsoft Defender for Cloud: Continuous security posture assessment across Azure resources, with recommendations mapped to common compliance frameworks.
• Encryption at rest and in transit: Azure storage services encrypt data by default, and Transport Layer Security (TLS) is enforced for data in transit.
• Role-Based Access Control (RBAC): Granular permissions prevent over-provisioning and limit the blast radius of compromised credentials.
• Compliance certifications: Azure holds a broad range of third-party compliance certifications (ISO 27001, SOC 2, HIPAA, GDPR alignment, and others), which can simplify an SMB’s own compliance audits.

Integrating storage, identity, and compute under one security framework reduces the attack surface created when data crosses boundaries between unrelated vendor systems. It also means security policies can be enforced consistently rather than replicated independently in each tool.

Public, Private, and Hybrid Cloud: A Practical Primer

Choosing the right cloud model is not a one-size-fits-all decision. The correct mix depends on your compliance obligations, data sensitivity, uptime requirements, and internal IT capacity. Here is how the three primary models compare.

Public Cloud

In a public cloud deployment, infrastructure is owned and operated by a cloud provider and shared — in logically isolated partitions — across many customers. Azure’s public cloud regions deliver Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) offerings: virtual machines, managed databases, Kubernetes clusters, serverless functions, and more.

Best suited for: workloads with variable demand, development and test environments, SaaS applications, and organizations that do not have strict data residency requirements. The public cloud offers the lowest barrier to entry and eliminates capital expenditure on physical hardware.

Trade-offs: Multi-tenant architecture means the perimeter is managed by the provider, not the customer. Compliance requirements in highly regulated industries (healthcare, finance, defence) may restrict which workloads can run in a fully public environment.

Private Cloud

A private cloud is dedicated to a single organisation. Resources are not shared with other tenants, providing stronger isolation and more granular control over the environment. Private clouds can be hosted on-premises in the organisation’s own data centre, or they can be hosted by a managed service provider in a dedicated facility.

Best suited for: organisations with strict regulatory, data residency, or security requirements — for example, handling personally identifiable information (PII) under GDPR, or processing payment card data under PCI DSS.

Trade-offs: Higher cost and operational overhead. Maintaining a private cloud requires either skilled internal staff or a managed service provider relationship. Azure Stack Hub is Microsoft’s offering for running Azure services on-premises in a private cloud configuration.

Hybrid Cloud

A hybrid cloud architecture combines on-premises or private cloud infrastructure with public cloud services, connected through secure networking (typically VPN or Azure ExpressRoute). An organisation might keep sensitive databases on-premises while running customer-facing web applications in the public cloud, bursting capacity into Azure during peak demand.

Best suited for: organisations that cannot move all workloads to the public cloud due to compliance or latency requirements, but still want the elasticity and managed services that public cloud provides for appropriate workloads.

Trade-offs: Hybrid architectures introduce additional complexity in networking, identity federation, and operational tooling. Azure Arc extends Azure management and policy to on-premises and multi-cloud environments, reducing some of that operational friction.

Choosing the Right Model for Your Organization

The decision between public, private, and hybrid cloud should be driven by a structured assessment of the following factors:

1. Data classification: Identify which data is sensitive or regulated and where it must reside.
2. Compliance requirements: Map applicable regulations (HIPAA, PCI DSS, GDPR, SOC 2) to the deployment models they permit.
3. Availability and recovery objectives: Define your Recovery Time Objective (RTO) and Recovery Point Objective (RPO) for each workload.
4. Internal IT capacity: A private cloud requires ongoing management expertise. If that capacity does not exist internally, a managed service provider or a managed Azure offering reduces the burden.
5. Total cost of ownership: Compare the full cost — including engineering time, licensing, and networking — not just the raw compute price.

For many SMBs, engaging a Microsoft-certified managed service provider (MSP) accelerates this assessment and reduces the risk of a poorly matched architecture. An MSP with Azure expertise brings experience across multiple client environments and can pattern-match your requirements against proven reference architectures.

Getting Started with Azure

Microsoft provides several resources for SMBs evaluating Azure:

• Azure free account: A limited free tier allows exploration of core services without immediate cost commitment.
• Microsoft for Startups (Founders Hub): Provides Azure credits and support for qualifying early-stage companies.
• Azure Pricing Calculator: Estimates monthly cost for a given architecture before committing resources.
• Microsoft Partner Network: The official directory for finding vetted Azure-certified managed service providers and consultants.

Cloud strategy is not a one-time decision. As your organisation grows, the workloads and compliance landscape evolve, and periodic architectural reviews ensure the cloud model continues to serve the business rather than constrain it.

The seven trends below were identified as major forces reshaping enterprise computing. While the original analysis was published in early 2020, most of these shifts have only accelerated and remain highly relevant to engineering teams today.

1. The Continued Rise of Hybrid Cloud

Enterprises rarely commit to a single cloud provider. Instead, they distribute workloads across multiple public clouds and on-premises infrastructure — a model known as hybrid or multi-cloud. Research firm Gartner predicted that by 2020, 75 percent of organizations would have deployed a multi-cloud or hybrid cloud model, driven by the need for flexibility, vendor independence, and regulatory compliance.

MarketsandMarkets projected the hybrid cloud market would grow from approximately $44.6 billion in 2018 to $97.6 billion by 2023, at a compound annual growth rate (CAGR) of around 17 percent. Key drivers include cost efficiency, scalability, operational agility, and the ability to keep sensitive workloads on-premises while shifting less critical services to public cloud providers.

For DevOps teams, hybrid cloud demands investment in unified management tooling, consistent identity and access controls across providers, and portable CI/CD pipelines that deploy reliably to any target environment.

2. Edge Computing Moves to Center Stage

The proliferation of IoT devices is generating data volumes that centralized cloud data centers struggle to absorb efficiently. Gartner forecast that 75 percent of enterprise-generated data would be created and processed outside a traditional centralized data center or cloud by 2025. An IDC FutureScape report projected that by 2022, 40 percent of enterprises would have doubled their IT asset spending in edge locations compared with core data centers.

Edge computing addresses this by moving compute and storage resources closer to where data is generated — factory floors, retail locations, vehicles, and remote facilities. Lower latency, reduced bandwidth costs, and improved resilience when connectivity to a central cloud is intermittent are the primary benefits.

For engineering teams, edge deployments introduce new operational challenges: managing a large fleet of distributed nodes, ensuring consistent software delivery, and maintaining security across potentially thousands of edge sites. Kubernetes distributions designed for resource-constrained environments (such as K3s) have emerged as a practical response to these challenges.

3. Hyperscale Data Centers Scale Up

Hyperscale refers to an IT architecture’s ability to scale compute, storage, and networking resources rapidly and proportionally in response to demand. Hyperscale data centers — operated by the largest cloud providers and technology companies — are designed from the ground up for elastic capacity.

MarketsandMarkets estimated the hyperscale data center market would grow from approximately $25 billion in 2017 to over $80 billion by 2022. Cisco predicted that by 2021, traffic within hyperscale data centers would quadruple and that hyperscale facilities would account for 55 percent of all data center traffic.

The modular design of hyperscale infrastructure means that individual components — storage nodes, compute blades, and network switches — can be added or replaced independently, giving operators precise control over capacity without large-scale forklift upgrades.

4. Disaster Recovery as a Service (DRaaS) Gains Momentum

Unplanned downtime is expensive. Gartner estimated the average cost of IT downtime at approximately $5,600 per minute, while IBM’s Cost of a Data Breach report put the average total cost of a breach at close to $3.92 million. Regulatory frameworks such as GDPR add a compliance dimension: organizations must demonstrate that they can recover systems and data within defined timeframes.

Disaster Recovery as a Service (DRaaS) addresses this by providing automated, cloud-based failover and recovery capabilities. Rather than maintaining expensive secondary data centers, organizations replicate workloads to a cloud provider and invoke recovery runbooks automatically. IDC projected the DRaaS market would reach $4.5 billion by 2020, growing at approximately 15 percent annually through 2023.

For DevOps practitioners, DRaaS fits naturally into infrastructure-as-code workflows: recovery environments can be defined declaratively, tested in isolation using chaos engineering practices, and spun up on demand rather than maintained continuously at full cost.

5. Hyperconverged Infrastructure (HCI) Sees Increased Adoption

Hyperconverged infrastructure (HCI) integrates compute, storage, and networking into a single software-defined platform managed through a unified interface. This approach eliminates much of the complexity associated with traditional three-tier data center architectures and reduces the operational burden on smaller IT teams.

Gartner predicted that by 2020, 20 percent of business-critical applications then running on three-tier infrastructure would migrate to HCI. Research firm Mordor Intelligence projected the HCI market would register a CAGR of over 13 percent from 2019 to 2024, driven by its simpler management model and lower total cost of ownership compared with disaggregated legacy infrastructure.

HCI is increasingly positioned as an on-premises alternative or complement to public cloud platforms, particularly for organizations with data sovereignty requirements or latency-sensitive workloads that cannot easily move off-premises.

6. AI Transforms Data Center Operations

Artificial intelligence is becoming an operational tool within data centers themselves, not only in the applications they host. AI-based systems can analyze historical telemetry to predict hardware failures before they occur, automatically rebalance workloads to optimize energy consumption, and flag anomalous network behavior that may indicate a security incident.

Gartner predicted that by 2020, 75 percent of organizations would experience visible business disruptions due to infrastructure and operations skill gaps — a significant increase from fewer than 20 percent in 2016. AI-driven automation is one response to this skills shortage, handling routine operational tasks and escalating only genuinely novel problems to human engineers.

Gartner also warned that data centers failing to prepare adequately for AI workloads risked becoming operationally or economically unviable. Modern GPU-dense infrastructure, high-bandwidth networking, and low-latency storage are prerequisites for serving AI inference and training at scale — requirements that differ substantially from traditional CPU-centric designs.

7. Containers Become the Standard Deployment Unit

Containers package application code together with its dependencies, making applications portable across development laptops, on-premises servers, and any public cloud. This “build once, run anywhere” property has made containers the default packaging format for microservices-based architectures.

Gartner predicted that by 2023, more than 70 percent of global organizations would be running more than two containerized applications in production, up from fewer than 20 percent in 2019. IDC projected that 95 percent of new microservices would be deployed in containers by 2021.

Container adoption is closely coupled with hybrid cloud growth: because containers abstract away underlying infrastructure differences, they simplify deployment across heterogeneous environments. Kubernetes has emerged as the dominant container orchestration platform, supported by all major cloud providers and a large ecosystem of tooling covering networking, storage, observability, and policy enforcement.

What These Trends Mean for Engineering Teams

These seven trends reinforce a consistent theme: enterprise IT infrastructure is becoming more distributed, more automated, and more software-defined. The boundaries between on-premises and cloud, between development and operations, and between application code and infrastructure configuration continue to blur.

• Platform engineers should invest in tooling that works consistently across hybrid and multi-cloud environments.
• Infrastructure teams should evaluate HCI and edge deployment models for workloads with latency or data-sovereignty constraints.
• SRE and operations teams should formalize DRaaS strategies and validate them regularly with automated failover tests.
• Developers should treat containers as the default packaging format and design services for portability from the outset.
• Leadership should plan for AI-driven automation to absorb routine operational tasks as the skills gap widens.

Cloud computing is not a destination but an ongoing architectural evolution. Teams that track these trends and build adaptable infrastructure will be better positioned to deliver reliable, scalable services regardless of how the technology landscape continues to shift.

Original analysis sourced from entrepreneur.com (2020). Market figures reflect analyst predictions available at the time of original publication.

The Strategic Context: Why VMware Had to Change

Heading into 2020, VMware (NYSE: VMW) was at a critical inflection point. As an enterprise company built on hypervisor-based virtualization—specifically its flagship vSphere platform—VMware was watching its traditional market erode as organisations shifted workloads to public cloud providers and adopted container-based architectures.

The broader industry had moved decisively toward Kubernetes as the de facto orchestration layer for containerised applications. For VMware, a company whose 600,000-strong vSphere customer base still ran the majority of enterprise workloads on virtual machines, this shift was both a threat and an opportunity. The question entering 2020 was whether VMware could successfully reposition itself as a hybrid cloud platform company—or whether it would cede ground to hyperscalers and cloud-native competitors.

The Acquisition Wave: Building a Kubernetes Portfolio

Rather than build Kubernetes capabilities entirely in-house, VMware embarked on an aggressive acquisition strategy. The key moves included:

• Pivotal Software ($2.7 billion): The cornerstone acquisition for VMware’s Kubernetes strategy. Pivotal brought the Cloud Foundry application platform, developer tooling, and expertise in cloud-native application development. VMware CEO Pat Gelsinger framed this as acquiring better cloud-native assets for significantly less than what IBM paid for Red Hat.
• Heptio: Founded by two of the original Kubernetes project leads from Google, Heptio gave VMware deep Kubernetes engineering expertise, community credibility, and enterprise support know-how.
• Bitnami: Known for its catalogue of pre-packaged open source applications, Bitnami brought a library of ready-to-deploy Helm charts and application packages relevant to Kubernetes environments.
• Carbon Black ($2.1 billion): A security-focused acquisition that extended VMware’s reach into endpoint detection and response (EDR), positioning the company to offer security as part of its infrastructure platform rather than as a bolt-on product.

For context, IBM’s acquisition of Red Hat—completed in 2019 for approximately $34 billion—gave IBM ownership of OpenShift, Red Hat’s enterprise Kubernetes distribution. VMware’s comparable Kubernetes story was being assembled at a fraction of that cost, though with different components and a different go-to-market approach.

VMware Tanzu: The Product Bet

The strategic output of the Pivotal and Heptio acquisitions was VMware Tanzu—a portfolio of products and services designed to help enterprises build, run, and manage Kubernetes-based applications. Tanzu was positioned to serve organisations that wanted to modernise their application stack without abandoning their existing VMware infrastructure investments.

Tanzu’s target audience is directly relevant to DevOps practitioners: platform engineering teams managing Kubernetes clusters across on-premises data centres and multiple clouds, and development teams that need a consistent application platform regardless of where workloads run.

Project Pacific: Kubernetes Inside vSphere

One of the most technically significant announcements tied to this period was Project Pacific, VMware’s initiative to embed Kubernetes directly into vSphere. The core idea was to allow vSphere administrators to provision and manage Kubernetes clusters using the same tools and workflows they already used for virtual machines—effectively making the Kubernetes control plane a native vSphere construct.

For DevOps engineers, this represented an attempt to close the gap between the VM-centric world of traditional operations and the container-centric world of modern application delivery. Instead of running Kubernetes on top of VMs in a separate management plane, Project Pacific aimed to make Kubernetes a first-class citizen within vSphere itself.

The strategic logic was clear: if VMware could get even a portion of its 600,000 vSphere customers to adopt Tanzu and Project Pacific, the potential revenue uplift was substantial.

The Delivery Challenge

Acquisitions create integration risk. Pivotal, Heptio, and Bitnami each had distinct cultures, engineering practices, and customer bases. Blending these into a coherent Tanzu product portfolio—while simultaneously maintaining the core vSphere business—was a significant engineering and organisational challenge.

Analysts tracking the stock at the time were projecting modest earnings growth for 2020 and 2021, with some suggesting that the full financial impact of the Pivotal acquisition had not yet been priced into estimates. The market, entering 2020, appeared to be in a wait-and-see posture.

The Dell Relationship: An Overhang on the Stock

A separate but important factor for investors in early 2020 was VMware’s ownership structure. VMware was majority-owned by Dell Technologies, which had returned to public markets after its acquisition of EMC (which owned a controlling stake in VMware). With Dell shares actively trading again, there was ongoing speculation about whether Dell would move to acquire VMware outright or execute a spin-off. This corporate structure uncertainty added complexity to the investment thesis independent of VMware’s operational performance.

Analyst Sentiment at the Time

According to the original InvestorPlace article, of the 24 analysts who had rated VMware at that point, 15 had issued buy ratings and eight had issued hold ratings. Investment firm Oppenheimer had reiterated an “outperform” rating with a price target of $200, citing what it described as a compelling risk/reward profile given that the market had not yet fully priced in VMware’s strategic transformation.

The stock had fallen roughly 25% from its 52-week high set in May 2019, and had declined approximately 5% in the first weeks of 2020 even as broader tech equities moved higher. Support around the $150 level was being watched as a key technical marker.

What This Meant for DevOps Practitioners

For engineers and platform teams, the business dynamics described above had concrete implications:

• Kubernetes tooling consolidation: VMware was signalling a long-term commitment to Kubernetes as a platform layer, which gave enterprise teams more confidence in betting on Tanzu for multi-year roadmaps.
• VM and container coexistence: Project Pacific’s approach rejected the binary choice between virtualisation and containers, offering a path for organisations that needed to run both.
• Vendor ecosystem complexity: With Carbon Black, Bitnami, Heptio, and Pivotal all absorbed into the VMware portfolio, practitioners needed to evaluate how these tools would integrate and whether the combined platform would deliver on its stated capabilities.
• Open source posture: VMware’s acquisitions leaned heavily on open source foundations (Kubernetes, Cloud Foundry, Helm). The company’s commitment to upstream contribution and open governance was a legitimate consideration for teams evaluating long-term platform lock-in.

Summary

VMware’s position entering 2020 was that of a well-resourced incumbent making a high-stakes bet on hybrid cloud and Kubernetes. The acquisitions of Pivotal, Heptio, Bitnami, and Carbon Black collectively outlined an ambitious portfolio strategy. Whether the company could execute the integration, win over its existing vSphere base, and compete credibly with Red Hat/OpenShift and cloud-native alternatives was the central question—one that would take several quarters to answer.

Original opinion piece by Chris Markoch, published at InvestorPlace.com. Chris Markoch disclosed that he did not hold a position in VMware or related securities at the time of writing.

KubeCon + CloudNativeCon 2019: The Cloud Native Ecosystem in Full Force

The Cloud Native Computing Foundation (CNCF) is responsible for governing the open source projects that form the critical building blocks of modern infrastructure. Its most prominent project, Kubernetes, has become the de facto standard for container orchestration at scale.

By 2019, CNCF had grown to over 20 hosted projects and 500 member organizations, representing one of the most active open source communities in the industry. KubeCon + CloudNativeCon 2019 saw a reported 50% increase in attendance compared to the previous year’s event in Seattle, drawing participants from traditional infrastructure incumbents like HPE and VMware through to early-stage startups.

More than 100 announcements emerged from the event. Below are ten of the most technically significant.

1. Helm 3 Is Launched

Helm — a CNCF incubating project and the most widely used package manager for Kubernetes — released its third major version at the event. Helm is maintained primarily by contributors from Microsoft, Google, Codefresh, and Bitnami.

The headline change in Helm 3 is the removal of Tiller, the server-side component that previously ran inside the Kubernetes cluster and was a persistent source of security and operational complaints. By eliminating Tiller, Helm 3 relies entirely on kubeconfig credentials and Kubernetes RBAC for access control, significantly simplifying the security model.

A migration path from Helm 2 to Helm 3 was published alongside the release to help teams upgrade existing chart deployments.

2. AWS, Intuit, and Weaveworks Collaborate on Argo Flux and the GitOps Engine

Two of the most prominent GitOps tools in the Kubernetes ecosystem — Argo CD (developed at Intuit) and Flux (developed at Weaveworks) — had significant functional overlap. At KubeCon 2019, the two teams announced a collaboration, informally referred to as Argo Flux, to converge their shared core logic into a common library called the GitOps Engine.

AWS added momentum to the collaboration by announcing it would integrate GitOps tooling based on this work into Amazon Elastic Kubernetes Service (EKS), and would use Flagger for canary deployments on AWS App Mesh.

The GitOps Engine project aimed to allow different CD tools to share a common, well-tested reconciliation layer, reducing duplication across the ecosystem.

3. Microsoft Brings Confidential Computing to Kubernetes

Microsoft announced support for confidential computing within Kubernetes, leveraging Intel Software Guard Extensions (Intel SGX). Confidential computing protects data in use — meaning data remains encrypted and isolated even while code is actively executing on the CPU, guarding against privileged-access attacks or compromised hypervisors.

Azure customers can create AKS clusters with SGX-capable nodes and install the confidential computing device plugin. Workloads built with the Open Enclave SDK can then be scheduled onto nodes that support Trusted Execution Environments (TEEs), enabling hardware-enforced isolation at the pod level.

4. Red Hat Launches CodeReady Workspaces 2.0

Red Hat’s CodeReady Workspaces, first announced in February 2019, is a browser-based, Kubernetes-native development environment built on the open source Eclipse Che IDE project. It is optimized for Red Hat OpenShift and RHEL environments.

Version 2.0 deepened the integration between the development environment and OpenShift production clusters, allowing developers to build and test applications in an environment that closely mirrors production. The goal is to eliminate the classic “works on my machine” problem by providing workspace definitions as code that all team members share.

5. Mirantis Launches Kubernetes as a Service (KaaS)

Mirantis announced a continuously updated, multi-cloud Kubernetes as a Service (KaaS) offering built on upstream, unmodified Kubernetes — without proprietary API extensions — to ensure application portability across cloud providers.

The announcement followed Mirantis’s high-profile acquisition of Docker’s enterprise business, signalling a strategic pivot toward Kubernetes-centric enterprise infrastructure.

6. O’Reilly Acquires Katacoda

Katacoda provides browser-based, interactive learning environments that spin up real Linux and Kubernetes infrastructure on demand — no local installation required. Users practice with the actual tools they would use in production, making it uniquely effective for hands-on DevOps and cloud native training.

O’Reilly’s acquisition of Katacoda extended its online learning platform with real-environment, scenario-based labs, enabling learners to move from reading about a technology to interacting with it without leaving the browser.

7. Portworx Launches PX-Autopilot

Portworx, a leading provider of container-native persistent storage, announced PX-Autopilot: a policy-driven automation layer that monitors storage consumption and automatically provisions additional block storage or resizes existing volumes when usage crosses a pre-configured threshold.

The product addresses a real operational pain point — stateful applications on Kubernetes that run out of disk space often fail in ways that are difficult to recover from. PX-Autopilot effectively brings autoscaling concepts, familiar from compute, to the storage layer in a cloud native context.

8. Diamanti Announces Spektra Hybrid Cloud Platform

Hyper-converged infrastructure vendor Diamanti announced Spektra, a hybrid cloud management platform designed to manage the full lifecycle of containerized workloads spanning on-premises infrastructure and public clouds.

Spektra provides a unified control plane — a single pane of glass — for managing storage across multiple registered Kubernetes clusters, and supports moving both applications and persistent data between clusters with minimal disruption. This addresses a key challenge for enterprises running workloads across heterogeneous Kubernetes environments.

9. Buoyant Announces Dive, a SaaS Control Plane for Kubernetes

Buoyant, the company behind the Linkerd service mesh, announced the private beta of Dive, a multi-tenant SaaS control plane for managing Kubernetes deployments at scale.

Dive is designed to make infrastructure changes auditable and collaborative: every change generates a permalink that teams can reference in Slack, issue trackers, or pull requests. Individual services get a dedicated homepage that can include SLOs, runbook links, and ownership metadata — effectively treating services as first-class objects with discoverable operational context.

Dive extends Linkerd’s observability and traffic management capabilities with a managed, multi-cluster control experience.

10. Rancher Announces General Availability of K3s for Edge Kubernetes

Rancher Labs announced the general availability of K3s, a lightweight, fully CNCF-certified Kubernetes distribution purpose-built for resource-constrained environments such as IoT devices, edge nodes, and ARM-based hardware.

K3s is distributed as a single binary with a minimal footprint, stripping out cloud-provider integrations and legacy features that are unnecessary at the edge. Rancher partnered with ARM to optimize K3s for ARM architectures, making it practical to run a conformant Kubernetes distribution on devices that would otherwise be unable to support a standard Kubernetes installation.

Key Takeaways

• Maturity of the ecosystem: Announcements like Helm 3 and the GitOps Engine reflect the community moving from experimentation to production-grade tooling with clearly defined operational models.
• Security is a first-class concern: Microsoft’s confidential computing integration shows hardware-level security entering the Kubernetes stack.
• Edge and IoT adoption: K3s from Rancher signals Kubernetes expanding well beyond the data centre.
• Storage automation closing the gap: PX-Autopilot and Spektra address persistent pain points around stateful workloads that have historically lagged behind compute automation.
• Developer experience investment: CodeReady Workspaces 2.0 and the Katacoda acquisition reflect growing investment in reducing friction for developers working in cloud native environments.

KubeCon + CloudNativeCon 2019 confirmed that Kubernetes has moved decisively from early adopter territory into mainstream enterprise infrastructure, with the surrounding ecosystem maturing rapidly to match.

Red Hat Inc., has announced Red Hat OpenShift 4.2, the latest version of Red Hat’s trusted enterprise Kubernetes platform designed to deliver a more powerful developer experience. Red Hat OpenShift 4.2 extends Red Hat’s commitment to simplifying and automating enterprise-grade services across the hybrid cloud while empowering developers to innovate and enhance business value through cloud-native applications.

Red Hat OpenShift 4.2 aims to make cloud-native technologies easier to use and more accessible for developers via capabilities that automate the set-up and management of Kubernetes environments. This enables developers to focus on building the next-generation of enterprise applications without requiring deep Kubernetes expertise.

The latest version of OpenShift also adds ready-to-use developer services that address needs around service mesh, serverless execution and cloud-native continuous integration/continuous delivery (CI/CD) pipelines, all of which are designed to help fuel developer productivity around Kubernetes-based applications:

·  Red Hat OpenShift Service Mesh, based on the Istio, Kiali, and Jaeger projects and enhanced via Kubernetes Operators, is designed to simplify the development, deployment, and management of microservices-based applications on OpenShift. 

·  Red Hat OpenShift Serverless, based on Knative and available as a Technology Preview, helps to lower costs by running applications that can scale down to zero while remaining responsive to user requests and then scaling up on-demand. 

·  Red Hat OpenShift Pipelines, in Developer Preview and available as a Kubernetes Operator, runs each step of the CI/CD pipeline in its container, allowing each step to scale independently to meet changing demands.

This release brings OpenShift closer to developers with Red Hat CodeReady Containers, which enables developers to install a pre-built OpenShift environment on a laptop for local development. This provides a framework that ties together container development tools with a local cluster, making it easier to build cloud-native applications locally and, when ready, deploy to a full OpenShift environment.

Cloud simplicity, wherever OpenShift runs

Enterprise IT has never been a homogenous, static series of deployments, and modern computing is not an exception. Hybrid deployments using components of on-premise infrastructure and cloud-based services are frequently being pursued by IT decision-makers and, to help ease the transition to the hybrid cloud, Red Hat OpenShift 4.2 is designed to deliver a more consistent, unified experience across disparate IT estates, adding:

·  Simplified, automated and faster installations of OpenShift Container Platform across public clouds, including AWS, Azure and Google Cloud Platform (GCP), and private clouds like OpenStack, backed by a consistent enterprise experience.

·  Migration tooling to help customers upgrade from OpenShift 3 to 4, offering a simpler, faster and more automated way to copy workloads from one OpenShift cluster to another. 

·  Container Storage Interface (CSI) drivers that provide a more consistent way for third party storage providers to plug into the Kubernetes ecosystem. This release also supports OpenShift Container Storage 4, currently in beta.

The industry’s most comprehensive enterprise Kubernetes platform

As organizations look to use enterprise Kubernetes to drive digital transformation, roadblocks can appear due to stringent internal and regulatory security standards. These requirements often limit the connectivity of sensitive data and workloads, requiring “air gaps” or being disconnected from the broader enterprise network. While good for security, it makes migrating and updating the applications and services used by these systems a challenging, manual process with dozens of steps. 

To help improve the OpenShift experience across heterogeneous deployments and systems, including those that handle sensitive information, Red Hat OpenShift 4.2 adds installer enhancements across environments, including support for disconnected installs. This enables customers to more easily deploy the latest version of the industry’s most comprehensive enterprise Kubernetes platform in environments that are not accessible via the Internet or those that are connected but maintain strict image testing policies.

Red Hat OpenShift 4.2 will be available in the coming weeks.

Supporting Quotes

“More and more organizations across industries and around the world trust Red Hat OpenShift to run their most critical business applications. We continue to prioritize making the next generation of enterprise open source technologies like Kubernetes even more accessible to developers while also keeping administrator priorities in balance. With these goals in mind, OpenShift 4.2 delivers on features to help customers accelerate application development and delivery,” said Ashesh Badani, senior vice president, Cloud Platforms, Red Hat.

“As Kubernetes-based container platforms for microservices evolve, new end-to-end toolchains are needed that can deliver code quickly and with fewer errors. The latest suite of OpenShift developer tools, especially OpenShift Pipelines, enables end-to-end CI/CD that automates code delivery to Kubernetes container platforms,” said Tom Petrocelli, research fellow, Amalgam Insights.

“At Google Cloud, we are committed to providing customers with the flexibility to deploy all types of enterprise workloads on GCP. Google and Red Hat share a long-standing collaboration across Kubernetes, support for the open-source community, and a mutual belief that open standards and open innovation are good for customers. We look forward to helping OpenShift customers leverage the power of Google Cloud through this partnership,” said Rayn Veerubhotla, director, partnerships at Google Cloud.

“We are excited to see the 4.2 release bringing new tooling and local development experiences to OpenShift users. Azure offers a range of OpenShift solutions including Azure Red Hat OpenShift, a fully managed OpenShift service jointly operated by Red Hat and Microsoft. With Red Hat OpenShift Container Platform 4 now generally available on Azure, enterprises can create hybrid cloud environments that can meet their current needs and also evolve to handle future requirements,” said Gabe Monroy, director of program management for the Azure Application Platform, Microsoft.

“As a company transitioning to containers and Kubernetes, we wanted to work with Red Hat given their deep expertise in Kubernetes for enterprises. In adopting Red Hat OpenShift 4, we can focus on our business of IT cybersecurity — our developers get to focus on code, while administrators can work with a trusted platform that can be easier to manage thanks to automated updates,” said Sean Muller, enterprise architect & technology leader, LiquidIT.

“We use Red Hat OpenShift as our Kubernetes solution, running a number of our critical systems in our private cloud environment. Using the automation in Red Hat OpenShift, we can continually deliver better functionality for our customers from our teams with short time-to-market and low risk,” said Alv Skjellet, head of IT platforms, Norsk Tipping.

“With our focus on the commercial transportation industry, we have been working with Red Hat given their expertise in cloud-native technology to power our fleet management solutions for transportation and logistics companies. Red Hat OpenShift 4 is already helping us to unify our work on this trusted enterprise Kubernetes platform across our hybrid cloud environment. Each cluster can be deployed using a single command. Kubernetes Operators help to enable “one-click” upgrades and lifecycle management. Operators provide automation for our application teams to move easily to an as-a-service model. We look forward to maximizing our use of the tools that are especially powerful for our developers so they can focus on innovations on our applications,” said Justin Newcom, vice president of Global Information Technology, Omnitracs.

What Is RDS on VMware?

In October 2019, AWS made its Relational Database Service (RDS) available for on-premises VMware deployments, extending a managed database experience beyond the public cloud for the first time. The launch gave VMware customers a way to manage SQL-based relational databases running inside their own data centers using the same tooling, operational model, and automation they rely on in the AWS cloud.

RDS on VMware handles common database operations including automated backup, patch management, replication, and disaster recovery — capabilities that DBAs already depended on in the public cloud version of RDS — but applied to workloads that must remain on premises for regulatory, latency, or architectural reasons.

Supported Databases at Launch

At general availability, RDS on VMware supported three database engines:

• Microsoft SQL Server
• PostgreSQL
• MySQL

Oracle Database was notably absent. According to contemporary reporting, one likely contributing factor was Oracle’s complex and often contentious licensing policy around VMware deployments — a source of compliance risk and cost that has spawned entire consulting practices. Additionally, AWS and Oracle had a very public rivalry at the time: Amazon had just announced it had completed a multi-year migration of its own consumer business off Oracle databases onto AWS-native services. AWS indicated it anticipated adding Oracle support in the future, based on customer demand.

Infrastructure Requirements

RDS on VMware was not described as a simple drop-in deployment. AWS evangelist Jeff Barr noted at launch that “the process is more involved than usual” and directed users to detailed documentation. The baseline infrastructure requirements for running the on-premises management plane were significant:

• vSphere version: 6.5 or higher
• Minimum cluster resources for management tooling: 24 vCPUs, 24 GiB of RAM, and 180 GB of storage
• Connectivity: Outbound internet access from the vSphere cluster and secure HTTPS connections to AWS public endpoints
• Additional resources are required on top of these minimums for each database instance customers deploy

The connectivity requirement — that on-premises clusters must maintain outbound connections to AWS — underlines the hybrid nature of the service. Management and automation live in AWS; compute and storage remain on premises.

Strategic Significance

AWS first announced RDS on VMware in August 2018, making the October 2019 general availability the culmination of a lengthy engineering effort. The launch was initially limited to AWS’s U.S. East region, scaled back from the original plan to simultaneously launch in U.S. West, Tokyo, and Frankfurt — suggesting AWS may have wanted to validate the offering at limited scale before broader rollout.

Keith Townsend, co-founder of consulting firm The CTO Advisor and a former VMware solutions architect, described it as a meaningful but complex undertaking:

“It’s a significant engineering effort, with a ton of questions around ensuring performance and support. I’m not clear on demand, but it’s a great opportunity to compete against Oracle, seeing Amazon is very public about their migration off Oracle to RDS.”

Keith Townsend, Co-founder, The CTO Advisor

Where RDS on VMware Fits in the Hybrid Cloud Landscape

The launch arrived at a moment when the hybrid cloud market was rapidly becoming more crowded and complex. Three contemporaneous developments provide useful context:

VMware Cloud on AWS

Generally available since 2017, VMware Cloud on AWS moves VMware’s Cloud Foundation stack onto AWS bare-metal infrastructure. It is sold and managed by VMware and had gained commercial traction by the time RDS on VMware launched. RDS on VMware complements this offering by moving AWS-managed services in the opposite direction — into customer-owned VMware environments.

AWS Outposts

Announced in November 2018, AWS Outposts was expected to reach general availability around AWS re:Invent 2019. Outposts deploys AWS-designed and AWS-managed hardware appliances on premises, replicating a broader slice of the AWS platform locally. RDS on VMware and Outposts address overlapping but distinct use cases: RDS on VMware integrates with existing VMware infrastructure, while Outposts introduces AWS-native hardware.

VMware and Oracle Partnership

Shortly before the RDS on VMware launch, VMware and Oracle announced a partnership through which Oracle would sell bundled SKUs of VMware software alongside Oracle Cloud Infrastructure. Unlike VMware Cloud on AWS — which is a managed service — the VMware-Oracle arrangement placed emphasis on customer self-management, representing a different philosophy toward hybrid cloud operations.

VMware’s Container Ambitions

At VMworld in August 2019, VMware announced Project Pacific — an effort to embed Kubernetes directly into vSphere. This signaled VMware’s intent to remain relevant not just for virtual machine workloads but for container-based architectures, broadening its appeal as enterprises modernized their application platforms.

Key Takeaways for DevOps and Infrastructure Teams

• Familiar RDS tooling on premises: Teams already using RDS in AWS can apply similar automation and operational patterns to on-premises VMware clusters without managing the database engine directly.
• Not a lightweight deployment: The management plane has substantial resource requirements separate from database instances — plan infrastructure accordingly.
• Connectivity is mandatory: The service requires persistent outbound connectivity to AWS endpoints. Air-gapped or network-restricted environments are not supported without additional architecture work.
• Oracle licensing complexity remains: Until Oracle support is added and licensing questions are resolved, shops running Oracle on VMware will not benefit from this offering.
• Part of a broader hybrid strategy: RDS on VMware sits alongside VMware Cloud on AWS and the anticipated AWS Outposts in a growing portfolio of options for organizations that cannot or will not move all workloads to the public cloud.

Original reporting by Chris Kanaracus for TechTarget’s SearchAWS, October 2019.

What Is Google Anthos?

Anthos is best understood as a brand and a product layer rather than a single technology. At its core, it is Google Kubernetes Engine (GKE) deployed in a certified configuration that can run on-premises hardware — via partners such as Dell and HPE — while maintaining a managed connection back to Google Cloud Platform (GCP).

An agent installed on the on-premises cluster maintains an encrypted connection to GCP. This allows operators to manage workloads, deploy applications, and scale services from the GCP console, regardless of whether those workloads run in a Google data centre or in their own. Reliable connectivity to GCP — for example via Google Cloud Interconnect — is a practical requirement for the management plane to function properly.

Anthos was first announced at Google Cloud Next in April 2019.

Why Kubernetes Is Central to Google’s Cloud Strategy

Kubernetes was originally developed at Google before being open-sourced and donated to the Cloud Native Computing Foundation (CNCF). Google’s long-term bet is that Kubernetes will become the dominant infrastructure abstraction layer — the standard API through which applications are deployed and managed — in the same way that virtual machines (VMs) became the default in the previous era of infrastructure.

This gives Google a strategic advantage over AWS and Microsoft Azure, both of which lead Google Cloud in overall market share. AWS and Azure both offer managed Kubernetes services (EKS and AKS respectively), but Kubernetes is peripheral to their core identity. For Google, Kubernetes — and Anthos built on top of it — is the centrepiece of its enterprise cloud push.

How Anthos Compares to Microsoft Azure Stack

The closest Microsoft equivalent is Azure Stack, which allows organisations to run a subset of Azure services in their own data centres. However, the two products take fundamentally different approaches:

• Azure Stack is a curated bundle of Azure services — including VMs, SQL databases, Azure Functions, and web application hosting — brought on-premises as a cohesive package of specific service types.
• Anthos is more of a blank container runtime canvas. Any application that can run in a container can run on Anthos, including applications sourced from the GCP Marketplace. The platform imposes fewer constraints on what you deploy.

A rough analogy: Anthos is similar to Azure Stack with only Azure Service Fabric — Microsoft’s own microservices and container orchestration platform — installed, rather than the full suite of higher-level services. The key difference is that Kubernetes has become an industry-wide standard, while Azure Service Fabric remains largely a Microsoft-internal technology.

Both products share a common goal: making the experience of managing application workloads on-premises as consistent as possible with managing them in the public cloud.

Handling Legacy Workloads

One practical barrier to Kubernetes adoption is the large installed base of VM-based workloads that organisations cannot easily rewrite as containers. Anthos addresses this through:

• VM-in-container support: Anthos can run VMs inside containers on Kubernetes, allowing legacy workloads to coexist on the same platform.
• Migrate for Anthos: A service designed to migrate existing VMware or Google Compute Engine VMs into containerised workloads running on Anthos, reducing the need for manual application refactoring.

Anthos Service Mesh and Cloud Run for Anthos

Two capabilities that have been added to the Anthos platform — Anthos Service Mesh and Cloud Run for Anthos — are worth understanding in context of the open-source ecosystem:

Anthos Service Mesh

Anthos Service Mesh is built on Istio, the open-source service mesh project. Istio handles traffic management, mutual TLS authentication, observability, and policy enforcement between services in a Kubernetes cluster. Anthos Service Mesh packages Istio with a managed control plane, GCP console integration, and pre-configured telemetry, reducing the operational complexity of running Istio at production scale.

Cloud Run for Anthos

Cloud Run for Anthos is based on Knative, the open-source project that adds event-driven, serverless-style workload execution on top of Kubernetes. Cloud Run for Anthos allows teams to deploy containerised applications without managing the underlying Kubernetes primitives directly, using a simplified push-to-deploy model similar to platform-as-a-service (PaaS). The managed version wraps Knative with GCP tooling and support.

In both cases, Google’s contribution is not new underlying technology, but productisation: managed configurations, integrated consoles, and support contracts that make open-source projects more accessible to enterprise teams that lack the operational expertise to run them independently.

The Bigger Picture: Google’s Enterprise Cloud Strategy

Anthos is Google’s primary vehicle for building enterprise cloud relationships. By embedding GKE deeply into on-premises infrastructure and offering a consistent management layer, Google aims to make GCP the natural management hub even for organisations that cannot or will not move all workloads to the public cloud.

The strategic logic is straightforward: if Kubernetes becomes the standard API for infrastructure — as VMs were before it — then Google’s early and deep investment in Kubernetes gives it a durable competitive position. Anthos is the product through which Google intends to convert that technical heritage into enterprise market share.

Whether that strategy succeeds depends on how well Google can support enterprise customers and build an ecosystem around Anthos — areas where AWS and Azure currently have a significant lead.

This article is based on reporting originally published by The Register in September 2019.

What is AWS re:Inforce?

AWS re:Inforce is a dedicated cloud security conference organized by Amazon Web Services. It was created as a spin-off of AWS re:Invent, the company’s flagship annual technology conference. According to accounts from attendees, the security tracks at re:Invent became so heavily attended that AWS decided those topics deserved a standalone event of their own.

The result is a focused, highly technical conference aimed squarely at security professionals. Unlike broad technology events where security is one track among many, re:Inforce is built around curated security content across AWS services, architectures, and platforms. Sessions are available for multiple levels of expertise, from practitioner fundamentals to advanced architecture deep-dives.

Who Should Attend AWS re:Inforce?

If your organization uses AWS at any level, re:Inforce is worth considering. The conference is designed for:

• Security engineers and architects working on AWS workloads
• CISOs and security leaders responsible for cloud governance
• DevSecOps practitioners integrating security into CI/CD pipelines
• Compliance and risk professionals managing multi-environment security posture

The format is compressed into two intensive days of dedicated security content, with limited marketing fluff. Even vendor-sponsored sessions tend to be technically substantive. One practical tip for prospective attendees: popular sessions fill up quickly. Book your preferred sessions as early as possible — ideally several weeks before the event — since many attendees have reported being unable to get into all the sessions they wanted.

Key Security Themes from AWS re:Inforce

The following themes emerged as major takeaways from the conference, reflecting broader trends in cloud security strategy:

1. Humans Don’t Scale

This is well-established thinking in DevOps, and it is becoming equally accepted in the security world. As organizations move workloads to the cloud, managing security policy across multiple dynamic environments — both from a configuration standpoint and an incident response standpoint — quickly becomes unmanageable at human speed. Rather than simply complaining about a shortage of security staff, forward-thinking organizations are consolidating their security toolchains into a smaller number of platforms that offer broad coverage, high automation potential, and compatibility with existing DevOps workflows.

2. Security Platforms Are Converging

IT operations have trended toward automation and efficiency for years. Security must follow the same path — acting as an enabler rather than a bottleneck. From a CISO perspective, the goal is unified governance across the entire network: both cloud and on-premises environments. Vendors that offer separate, disconnected products for cloud and on-prem are increasingly seen as unsustainable. A single, unified solution that spans all environments is the direction the industry is moving.

3. Cloud Migration Is Hard — Especially for Security

Migrating workloads to AWS is complex. Migrating security policy is even harder. Organizations frequently find that existing security controls either do not exist in the cloud model or are not enforceable in the same way. Legacy security tools built for on-premises environments are often insufficient or ill-suited to maintaining an equivalent security posture in the cloud. A successful migration requires explicitly planning for this gap and replacing or adapting controls accordingly.

4. Refresh Your Threat Model

As organizations adopt new cloud services and constructs, they need to revisit what actually requires protection. Threat models built for traditional on-premises architectures may not account for cloud-native services, shared responsibility boundaries, or hybrid topologies. Tools like AWS Control Tower — which reached general availability around the time of the conference — help organizations establish and enforce governance policies across AWS accounts. For complex hybrid or cloud-only network topologies, a solution that can apply overlay policy across both cloud and on-premises assets becomes essential.

5. APIs Are Central to Modern Security Operations

As network architectures grow in complexity, the idea of a human manually monitoring or maintaining security posture becomes increasingly unrealistic. The SOAR (Security Orchestration, Automation, and Response) space is a direct response to this problem. Organizations are moving away from large, human-staffed Security Operations Centers toward automated playbooks that handle incidents according to pre-defined scripts. This approach addresses both the staffing challenge and the inconsistency that comes with human-driven incident handling. Security tools that do not expose APIs or support scripted policy creation are poorly positioned for cloud-native environments.

The Broader Implication: Unified, Automated Cloud Security

The themes from re:Inforce point toward a consistent conclusion: modern cloud security must be dynamic, automated, and environment-agnostic. Modern infrastructures can change thousands of times in a single day through auto-scaling, container orchestration, and continuous deployment pipelines. Security policy needs to keep pace with that cadence — applying and updating controls as fast as the infrastructure changes beneath them.

Security should not be a blocker to delivery. A well-designed security architecture integrates with the same APIs and automation tooling that the rest of the engineering organization uses — rather than requiring manual, UI-driven configuration that cannot scale.

Summary

AWS re:Inforce is a technically rigorous, security-focused conference for practitioners working in AWS environments. Its core themes — automation at scale, platform convergence, dynamic threat modeling, and API-driven security — reflect the direction cloud security is heading industry-wide. For security engineers and architects invested in AWS, it offers a concentrated opportunity to engage with deep technical content and consult directly with AWS security experts.

By early 2019, cloud computing had moved well beyond its origins as a low-cost alternative to on-premises servers. It had become the default platform for enterprise digital transformation. The combination of low-cost infrastructure and high-value managed services was accelerating cloud adoption faster than most analysts had predicted even a few years earlier. Here are the ten trends that were expected to define cloud computing in 2019.

1. Enterprise Cloud Spending Would Surge

The expectation was that 2019 would see a significant jump in enterprise cloud budgets, driven by two converging needs: modernizing legacy applications and extracting value from years of accumulated data. Organizations were expected to invest heavily in SaaS platforms while simultaneously adopting cloud-native technologies — particularly Kubernetes, containers, and serverless functions — to support new architectures and faster release cycles.

The underlying logic was straightforward: once leadership recognized cloud as a competitive differentiator rather than just a cost-saving measure, budget constraints eased and adoption accelerated across the board.

2. Multi-Cloud and Hybrid-Cloud Orchestration Would Gain Momentum

By 2019, many enterprises had already committed to both on-premises infrastructure and at least one public cloud provider. The resulting hybrid and multi-cloud environments introduced serious operational complexity — managing costs, enforcing policies, and maintaining visibility across disparate platforms became full-time engineering challenges.

The prediction was that dedicated orchestration platforms would emerge to address this complexity. Rather than teams juggling separate consoles for AWS, Azure, GCP, and private data centers, unified orchestration layers would provide a single pane of glass for cost tracking, workload placement, and governance. This trend proved accurate and has since matured into a category of its own, with tools like Terraform, Crossplane, and vendor-native control planes filling this role.

3. Open-Source Would Drive Technology Democratisation

The democratisation of cloud infrastructure tools was already underway by 2019, led largely by the open-source ecosystem. Technologies that had previously required specialized vendors — hypervisors, cloud management platforms, container runtimes, service meshes — were becoming available as community-maintained open-source projects.

Developer communities were identified as the primary driver of this shift. As developers gained more influence over infrastructure decisions (a trend closely related to the rise of DevOps culture), open-source tools became the default starting point for new projects, reducing lock-in and lowering the cost of experimentation.

4. Specialized Cloud Service Models Would Proliferate

The cloud service model had already expanded well beyond IaaS, PaaS, and SaaS by 2019. The prediction was that increasingly specialized service categories would gain traction, including:

• iPaaS (Integration Platform as a Service) — for connecting disparate applications and data sources
• dPaaS (Data Platform as a Service) — managed data pipeline and analytics environments
• BaaS (Blockchain as a Service) — hosted distributed ledger capabilities
• FaaS (Function as a Service) — serverless compute, exemplified by AWS Lambda and Azure Functions
• mBaaS (Mobile Backend as a Service) — managed backends for mobile application developers

This fragmentation reflected a broader shift: cloud providers were no longer competing solely on raw infrastructure price, but on the depth and breadth of managed services they could offer to reduce developer toil.

5. Full-Stack Developers Would Need Data Engineering Skills

As organizations moved more of their workloads to the cloud and adopted data-driven decision making, the boundaries between application development and data engineering began to blur. The prediction was that full-stack engineers who understood distributed data processing frameworks — particularly Apache Spark — would become significantly more valuable.

This reflected a real skills gap: many development teams knew how to build APIs and front-end interfaces, but lacked the background to design efficient data pipelines or work with large-scale streaming and batch processing systems. Cloud platforms lowered the barrier to entry for these capabilities, but developers still needed a conceptual foundation to use them effectively.

6. Cloud-Based AI and Machine Learning Would Accelerate

The major public cloud providers had begun embedding AI and machine learning services into their platforms around 2017. By 2019, these offerings had matured into comprehensive ecosystems covering managed model training, inference APIs, AutoML tooling, and pre-built cognitive services for vision, speech, and natural language processing.

The expectation was that 2019 would see accelerated enterprise adoption, as organizations that had been evaluating or piloting AI capabilities would move toward production deployments. This prediction aligned with the broader digital transformation narrative: companies that delayed adoption risked falling behind competitors who were using AI to optimize operations, personalize customer experiences, and reduce costs.

7. Organizations Would Create Dedicated Digital Skills Roles

A recurring challenge for enterprises adopting cloud technology was the gap between available training programs and the practical skills their engineering teams actually needed. Generic certification courses often failed to align with a team’s specific technology stack or business context, leaving a disconnect between training investment and measurable outcomes.

The prediction was that organizations would respond by creating dedicated Digital Skills Officer roles — similar in concept to a Chief Learning Officer — positioned to work alongside CTOs and CIOs. These roles would be responsible for translating technical roadmaps into targeted learning programs, ensuring that training efforts produced engineers capable of executing on the organization’s specific cloud strategy rather than passing generic exams.

8. Apache Spark and Data Processing Expertise Would Become a Core Engineering Skill

Related to prediction five, this point emphasized the specific importance of Apache Spark as a framework that full-stack and generalist engineers would increasingly encounter. As cloud-based data platforms made it easier to spin up Spark clusters without managing underlying infrastructure, the expectation was that familiarity with Spark’s programming model — DataFrames, RDDs, streaming APIs — would become a standard part of the cloud engineer’s toolkit rather than a specialized data engineering niche.

This prediction reflected the growing importance of real-time and batch analytics as first-class features of cloud-native applications, rather than separate analytical workloads handled by a separate data team.

9. Container Adoption Would Accelerate, Especially in Microservices

Containers package an application with all of its dependencies — libraries, binaries, configuration — into a single portable unit that runs consistently across environments. By 2019, containers had moved from early-adopter experimentation to mainstream enterprise consideration, driven in large part by Docker’s popularity and the rapid maturation of Kubernetes as an orchestration standard.

The prediction was that 2019 would see continued acceleration, particularly in microservices architectures where containers provide an ideal deployment unit: lightweight, independently deployable, and portable across cloud providers and on-premises infrastructure. Managed Kubernetes services from AWS (EKS), Google (GKE), and Azure (AKS) were all either recently launched or expanding, lowering the operational barrier to container adoption significantly.

10. Cloud Would Extend and Enhance Legacy Applications

Early cloud narratives often assumed that organizations would wholesale migrate their legacy systems to the cloud. In practice, this rarely happened on the expected timeline. Most enterprises found that core legacy systems — billing platforms, ERP systems, mainframe workloads — were too deeply embedded in business processes to replace quickly, and the risk of disruption outweighed the benefits of a full migration.

The more realistic prediction for 2019 and beyond was a hybrid approach: legacy systems would remain in place, but cloud services would be used to augment them. Specific augmentation patterns included:

• Connecting legacy data stores to cloud-based analytics and machine learning pipelines
• Using edge computing for IoT workloads that legacy systems were not designed to handle
• Wrapping legacy functionality in modern APIs to enable new front-end experiences
• Offloading compute-intensive workloads to cloud functions while keeping core logic on-premises

This pattern — often called “cloud augmentation” or the “strangler fig” migration pattern — became one of the dominant enterprise cloud strategies precisely because it reduced risk while delivering incremental value.

Looking Back: How Accurate Were These Predictions?

In retrospect, most of these 2019 predictions proved directionally correct. Multi-cloud complexity became one of the defining operational challenges of the following years. Container and Kubernetes adoption continued at pace. AI and ML services expanded dramatically across all major cloud platforms. The skills gap in cloud and data engineering remains a persistent issue for engineering leaders today.

The prediction that aged least well — or at least evolved significantly — was the focus on Apache Spark as a specific skill for full-stack developers. While Spark remains important in data engineering, the broader ecosystem around managed data services (Databricks, BigQuery, Snowflake, dbt) shifted how practitioners interact with distributed data processing, often abstracting away the framework-level details that the 2019 prediction emphasized.

The core insight from this 2019 analysis remains valid: cloud is not simply an infrastructure cost play. It is the platform on which enterprise competitiveness is built, and the organizations that invest in skills, orchestration, and architecture — not just compute budgets — are the ones best positioned to benefit from it.

Why Legacy Migration Is a Pressing Problem

Modern organizations grow in two broad ways: organically, by building internal capabilities over time, or through acquisition, by absorbing companies that already have what they need. Both paths leave behind the same artifact—legacy infrastructure that was never designed for today’s cloud-native world.

For organically grown businesses, years of incremental decisions accumulate into a tightly coupled, difficult-to-change stack. For companies that grow through acquisition, the problem compounds: each acquired company arrives with its own tooling, platforms, and operational practices. The result is a heterogeneous environment that is expensive to run, difficult to automate, and slow to evolve.

Cloud adoption promises real relief. Moving workloads to cloud infrastructure can significantly reduce IT overhead costs and gives teams the elasticity to scale resources up or down on demand. Cloud environments also lend themselves well to automation, freeing engineering time that would otherwise be spent on manual operations.

Greenfield vs. Brownfield: The Reality Most Teams Face

In an ideal world, every team would start with a greenfield: a blank slate on which to design the optimal infrastructure from scratch. In practice, most engineering organizations operate in a brownfield—an environment layered with old systems, accumulated technical debt, and operational habits that resist change.

A brownfield environment is not inherently bad. Legacy systems often still deliver meaningful business value; that is precisely why they have not been decommissioned. The challenge is integrating them with modern cloud capabilities without breaking what already works, or writing off the investment those systems represent.

Hybrid cloud—a model that spans on-premises or legacy infrastructure alongside public or private cloud resources—has emerged as the practical answer for most enterprises. It allows organizations to modernize incrementally rather than all at once, which reduces risk and preserves continuity.

The Core Technical Challenge: Infrastructure Fragmentation

Even after you commit to a cloud strategy, fragmentation remains a serious obstacle. The major cloud providers each offer their own proprietary services and managed platforms. Teams that distribute workloads across providers quickly discover that each environment operates differently, making it hard to build consistent deployment pipelines, monitoring setups, or operational runbooks.

The result is what some engineers call “islands of infrastructure”—clusters of compute and storage that are built, deployed, and operated in incompatible ways. A DevOps team cannot maintain a truly consistent software delivery practice if the underlying platform changes significantly from one environment to the next.

Normalization: A Strategy for Consistency Across Environments

One of the most effective responses to infrastructure fragmentation is normalization—deliberately choosing tools and abstractions that behave consistently regardless of the underlying provider or hardware.

Open source technologies are well-suited to this role because they are not tied to any single vendor and can be deployed across a wide range of infrastructure targets. A normalized stack might look like this:

• Kubernetes for container orchestration, providing a consistent API for deploying and managing containerized workloads whether you are on AWS, GCP, Azure, or on-premises hardware.
• OpenStack for managing virtual machines in private cloud or data center environments, giving teams a familiar control plane that works independently of hyperscaler APIs.
• Serverless frameworks (such as Fission or OpenFaaS) for function-based workloads, abstracting away the underlying compute so that functions can run on any compliant platform.

The goal of this kind of normalized stack is not to eliminate all complexity—that is impossible. Instead, it is to contain complexity. Proprietary or environment-specific concerns get isolated to defined boundaries, while the majority of your application code and operational tooling can remain platform-agnostic.

Migration Approaches: Choosing the Right Strategy

There is no single right way to move a legacy system to the cloud. The appropriate approach depends on the system’s complexity, business criticality, and how much architectural change is acceptable. Common strategies include:

• Rehosting (lift and shift): Moving workloads to cloud VMs with minimal or no code changes. Fast and low-risk in the short term, but does not take advantage of cloud-native features and may carry the same operational overhead into the new environment.
• Replatforming: Making targeted changes—such as migrating from a self-managed database to a managed cloud database service—without rewriting the application. A middle path that captures some cloud benefits without full re-architecture.
• Refactoring (re-architecting): Fundamentally redesigning the application for cloud-native patterns, such as breaking a monolith into microservices. Delivers the greatest long-term benefits but requires the most time, skill, and risk tolerance.
• Retaining: Deliberately keeping certain systems on-premises because migrating them is not cost-effective or technically feasible at this time. A valid choice, and often part of a hybrid cloud strategy.
• Retiring: Decommissioning systems that no longer serve a business purpose. Often uncovered during migration planning and represents real cost savings.

For many organizations, the most pragmatic path is a combination: lift-and-shift in the short term to get off aging hardware, followed by incremental refactoring as time and budget allow.

Embrace Change as an Organizational Capability

Technical strategy alone is not enough. One of the consistent lessons from DevOps practice is that tooling changes succeed or fail based on whether the organization has also changed how it thinks about work.

The infrastructure ecosystem has evolved dramatically over the past fifteen years. Configuration management tools like Puppet and Chef established the idea that infrastructure should be defined as code. OpenStack brought open source principles to private cloud. Vagrant and later Docker made reproducible development environments mainstream. Kubernetes moved container orchestration from a niche concern to an industry standard.

Each of these shifts required teams to retire familiar practices and invest in new ones. Organizations that built a habit of adaptation moved through these transitions faster and with less disruption. Those that resisted found themselves repeatedly catching up rather than keeping pace.

For legacy migration specifically, this means building internal processes that support ongoing change: structured learning time, blameless retrospectives, cross-functional migration working groups, and executive support for the inevitable period of reduced velocity during transition.

Containing Complexity Rather Than Eliminating It

A useful mental model for legacy migration is that complexity cannot be destroyed—it can only be moved or reorganized. Every system that has run in production for years has accumulated essential complexity: edge cases that the code handles, operational knowledge that lives in runbooks, integrations with other systems that depend on specific behaviors.

The goal of a migration is not to produce a magically simple system. It is to move complexity from places where it is hard to manage—tangled codebases, undocumented server configurations, manual deployment processes—into places where it is easier to reason about and operate, such as well-defined service boundaries, infrastructure-as-code repositories, and automated pipelines.

Normalization supports this by providing consistent abstractions. When your containerized workloads all run through Kubernetes regardless of cloud provider, the operational complexity of multi-cloud management is largely contained at the cluster configuration layer rather than scattered throughout your application code and deployment scripts.

Practical Recommendations for Teams Starting a Legacy Migration

1. Inventory before you migrate. Understand what you actually have. Document dependencies, data flows, integration points, and SLAs. Many migrations stall because teams discover mid-flight that a system has more connections than anyone realized.
2. Prioritize by risk and value. Not every system needs to move at the same time or in the same way. Identify which systems are expensive to run on-premises, which are under active development, and which are stable but business-critical. Use that analysis to sequence your migration.
3. Standardize your toolchain early. Decide on your container runtime, orchestration platform, CI/CD pipeline, and observability stack before you begin migrating workloads. Retrofitting these after the fact is significantly harder.
4. Use infrastructure as code from day one. Whether you use Terraform, Pulumi, Ansible, or another tool, define your cloud infrastructure in version-controlled code. This makes environments reproducible and makes changes reviewable.
5. Plan for the hybrid period. Most migrations take longer than initially projected. Design your architecture so that legacy systems and cloud-native systems can communicate reliably during the transition, not just after it is complete.
6. Measure operational outcomes. Define what success looks like before you start: deployment frequency, change failure rate, mean time to recovery, infrastructure cost. Track these throughout the migration so you can demonstrate progress and catch regressions early.

Summary

Migrating legacy systems to the cloud is one of the most consequential and difficult infrastructure challenges an engineering organization can face. There is no silver bullet, and the right approach varies by organization, system, and business context. However, a few principles hold broadly: invest in normalization through open source, provider-agnostic tooling; choose your migration strategy based on the specific needs of each system; build organizational practices that support ongoing change; and treat complexity as something to be contained rather than eliminated.

A hybrid cloud model that combines the best of legacy investment with cloud-native capabilities gives most organizations the most realistic path to a modernized, cost-effective, and operationally sustainable infrastructure.

Overview: Why 2019 Was a Pivotal Year for Cloud Architecture

Cloud computing entered 2019 no longer as an emerging concept but as an operational necessity. Enterprises of all sizes—from startups to global conglomerates—were actively migrating workloads, re-architecting applications, and rethinking infrastructure strategies. The foundational service models—Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)—had matured, and the conversation shifted toward how organizations could extract more value from cloud-native patterns while managing security, compliance, and cost.

For DevOps engineers and cloud architects, understanding these trends was critical for making informed decisions about tooling, deployment strategies, and long-term infrastructure roadmaps. Below is a structured breakdown of the major forces reshaping cloud architecture heading into 2019.

1. 5G Integration with Cloud and IoT

The gradual rollout of 5G cellular networks created new architectural possibilities—particularly for Internet of Things (IoT) systems. By pairing 5G’s low-latency, high-bandwidth connectivity with cloud compute resources, engineers gained a pathway to push data processing closer to the edge while offloading heavier analytical workloads to centralized cloud infrastructure.

Key technical implications of 5G for cloud architects:

• Edge computing enablement: 5G reduces round-trip latency between IoT devices and edge nodes, enabling real-time processing use cases that were previously impractical over 4G.
• Increased data velocity: Higher throughput means cloud ingestion pipelines (e.g., Kafka, AWS Kinesis, Azure Event Hubs) must be designed to handle greater volumes of streaming data from connected devices.
• Network slicing: 5G introduces the concept of virtualized, isolated network segments, which maps well onto multi-tenant cloud architectures.

It is worth noting that the 5G rollout in 2019 was uneven globally, with significant geopolitical, financial, and regulatory complexity affecting deployment timelines. Cost of infrastructure upgrades, net neutrality debates, and competitive tensions between the US and China all played into the pace of enterprise adoption.

2. Quantum Computing on the Horizon

In 2019, quantum computing remained largely experimental but attracted serious investment from major cloud providers including AWS, Microsoft Azure, IBM, and Google Cloud. For cloud architects, this period represented a monitoring phase rather than an adoption phase—understanding where quantum acceleration could eventually complement classical cloud workloads.

Potential long-term applications discussed at the time included:

• Optimization problems: Supply chain routing, financial portfolio optimization, and logistics scheduling—problems too large for classical solvers.
• Cryptography: Quantum computing posed a future challenge to existing public-key encryption standards, prompting early discussion of post-quantum cryptography.
• Machine learning acceleration: Certain classes of ML algorithms could theoretically benefit from quantum speedups, though practical implementations remained years away.

For DevOps teams, the practical action in 2019 was awareness and monitoring of quantum-as-a-service offerings from major cloud vendors, rather than immediate integration into production pipelines.

3. Hybrid Cloud: The Enterprise Default Architecture

Hybrid cloud—the combination of on-premises private infrastructure with public cloud services—became the dominant enterprise architecture pattern entering 2019. It addressed a real tension: organizations needed the scalability and cost efficiency of public cloud while retaining control over sensitive data and legacy systems that couldn’t be easily migrated.

Hybrid cloud architectures typically involve:

• Secure interconnects: VPNs or dedicated links (e.g., AWS Direct Connect, Azure ExpressRoute) between on-premises data centers and cloud VPCs/VNets.
• Consistent identity and access management: Federated identity (e.g., LDAP/AD integration with cloud IAM) to manage access across environments.
• Workload placement policies: Keeping regulated or sensitive data (financial records, PII) on private infrastructure while running less sensitive workloads—email campaigns, web frontends, analytics jobs—on public cloud.
• Unified management planes: Tools like HashiCorp Terraform, Ansible, or vendor-native solutions (Azure Arc, AWS Outposts) to manage resources consistently across environments.

The hybrid model reduced migration risk by allowing incremental transitions rather than forcing a wholesale lift-and-shift, which was a significant factor in its widespread adoption.

4. GDPR Compliance in Cloud Environments

The General Data Protection Regulation (GDPR) came into force in May 2018, and its compliance implications were still being worked through by organizations moving into 2019. For cloud teams, GDPR introduced concrete engineering requirements around how personal data belonging to EU citizens is stored, processed, and transferred.

Cloud-specific GDPR considerations for DevOps engineers:

• Data residency controls: Ensuring that data is stored only in approved geographic regions. Most major cloud providers offer region-locking and data residency guarantees.
• Identity and Access Management (IAM): Fine-grained access controls, audit logging, and least-privilege principles to demonstrate accountability under GDPR’s data controller obligations.
• Encryption at rest and in transit: Using cloud-native KMS services or third-party solutions to protect personal data throughout its lifecycle.
• Serverless and container security: Ensuring that modern compute paradigms (Lambda, Kubernetes pods, container instances) don’t inadvertently expose personal data through misconfigured permissions, environment variables, or log outputs.
• Data retention and deletion pipelines: Building automated mechanisms to honour right-to-erasure requests across distributed data stores.

Organizations that had rushed to cloud without a security-first mindset found themselves needing to retrofit compliance controls—an expensive and disruptive process. Building GDPR controls into CI/CD pipelines and infrastructure-as-code templates from the start was the recommended approach.

5. AI and Machine Learning Platforms on Cloud Infrastructure

AI and ML workloads became a primary driver of cloud adoption in 2019. The ability to provision GPU clusters on demand, access managed ML services, and store large training datasets without upfront hardware investment made cloud the natural home for data science and AI engineering work.

Cloud AI platforms—such as AWS SageMaker, Google AI Platform (now Vertex AI), and Azure Machine Learning—offered capabilities that directly benefit DevOps and ML engineering teams:

• Collaborative experiment tracking: Shared environments where data scientists and ML engineers can version datasets, model artifacts, and training runs.
• Automated pipeline orchestration: Managed pipelines for data preprocessing, model training, evaluation, and deployment—reducing manual intervention and enabling reproducibility.
• Cost reduction through automation: Spot/preemptible instance scheduling for training jobs, auto-scaling inference endpoints, and deduplication of data preprocessing work across teams.
• Data governance tooling: Metadata catalogues, lineage tracking, and access controls to ensure data quality and regulatory compliance across ML workflows.

For DevOps practitioners, AI workloads introduced the concept of MLOps—applying CI/CD, infrastructure-as-code, and monitoring disciplines to machine learning model lifecycle management.

6. Multi-Cloud Strategies: Flexibility at a Cost

Multi-cloud—running workloads across two or more public cloud providers simultaneously—gained traction as organizations sought to avoid vendor lock-in, improve resilience, and optimize cost by placing workloads on the provider best suited to each use case.

Key drivers and architecture patterns:

• Resilience and disaster recovery: Distributing critical workloads across AWS and GCP (for example) so a provider-level outage doesn’t cause total service failure.
• Best-of-breed service selection: Using GCP’s BigQuery for analytics, AWS S3 for object storage, and Azure Active Directory for identity—selecting each cloud’s strongest services rather than committing to one ecosystem entirely.
• Regulatory and data sovereignty requirements: Some jurisdictions or contracts require workloads to run on specific providers or in specific regions, making multi-cloud a compliance necessity.
• Negotiating leverage: Maintaining relationships with multiple vendors provides commercial flexibility during contract renewals.

However, multi-cloud also introduces real operational complexity that DevOps teams must plan for:

• Inconsistent IAM models, networking primitives, and API patterns across providers require abstraction layers or provider-agnostic tooling (Terraform, Kubernetes, Crossplane).
• Data egress costs between clouds can be significant and must be modelled into architecture decisions.
• Security policy enforcement becomes more difficult without a unified control plane—a common source of misconfiguration incidents.
• Observability requires aggregating metrics, logs, and traces from disparate provider-native monitoring tools into a single platform (e.g., Datadog, Grafana, OpenTelemetry).

Conclusion: Enduring Lessons for Cloud Architects

The cloud trends of 2019 established architectural patterns and engineering disciplines that remain relevant today. Hybrid and multi-cloud architectures are now standard practice. GDPR compliance engineering is a permanent fixture of data platform design. AI/ML platforms have spawned the MLOps discipline. And the intersection of edge computing with 5G continues to evolve as a frontier for cloud architects working on IoT and real-time systems.

The recurring theme across all these trends is that cloud adoption without deliberate architecture leads to compounding technical debt—in security, cost, and operational complexity. The most successful DevOps and platform engineering teams treated each of these trends not as marketing categories but as engineering problems requiring careful design, measurement, and iteration.

Cloud infrastructure continues to accelerate. The fundamentals—security by design, infrastructure as code, observability, and iterative delivery—remain the reliable foundation on which to evaluate and adopt whatever emerges next.

Cloud Migration Is Only the Beginning

Cloud has become a foundational component of modern business strategy, driven largely by the race to digitally transform. Organizations worldwide are migrating applications and services to the cloud, attracted by reductions in capital and operational expenditure. But the migration itself is not the destination — it is the starting line.

When harnessed correctly, cloud infrastructure becomes a platform for continuous innovation, enabling business models and use cases that were simply not feasible a few years ago. No group inside an organization feels this more acutely than DevOps teams, who are responsible for continuous delivery and deployment pipelines. Yet in many organizations, the people making cloud strategy decisions are rarely the engineers doing the day-to-day delivery work.

For digital transformation (DX) to succeed, DevOps teams must be agile, efficient, and capable of delivering higher-quality software at greater velocity — while working collaboratively across quality assurance (QA), security, development, and IT operations functions.

DevOps Maturity: Culture and Visibility

At its core, the DevOps function exists to produce new software based on business needs, at high velocity, under real-world constraints, and at the highest possible quality. High velocity in this context means a continuous delivery pipeline that can, in advanced organizations, support multiple production releases per day — each requiring cycles of build, test, and integration before deployment. High quality means reliable, responsive services with minimal downtime and a positive customer experience.

The maturity of a DevOps organization can be assessed across two key dimensions, loosely analogous to frameworks such as the Software Capability Maturity Model (SW-CMM) developed at the Software Engineering Institute at Carnegie Mellon University:

• Cultural maturity: The degree to which teams collaborate effectively and take shared ownership of outcomes, rather than optimizing for function-specific metrics in isolation.
• Holistic visibility and situational awareness: The availability of telemetry and KPIs that are meaningful across all functional teams — development, QA, operations, and security — rather than being siloed within each group.

In less mature organizations, each team focuses on its own domain. Developers track release frequency. QA tracks test coverage and defect rates. Operations monitors production performance. Each group optimizes its own work without a shared view of the system as a whole. This siloed approach creates bottlenecks, slows feedback loops, and increases the time it takes to detect and resolve issues.

Mature DevOps organizations break through these silos. They establish a common situational awareness — a shared, real-time understanding of system state that enables all teams to work from the same picture. This shared awareness is what allows development, QA, operations, and security to act as a coherent unit rather than a collection of competing departments.

How Visibility Transforms the Delivery Pipeline

To understand the value of visibility, consider a typical software delivery cycle in an organization that lacks it:

1. Developers write and build code.
2. Code is handed off to QA for testing.
3. A release manager oversees integration and deployment.
4. Operations identifies a production issue — often one that only appears at scale.
5. Development must rapidly diagnose and patch the issue, often without full context about what is happening in production.

At each handoff, teams are working with incomplete information about what happened before and after their stage. The feedback loop is slow and lossy.

When all teams share a common view of the system — the same telemetry, the same dashboards, the same real-time signals — this dynamic changes fundamentally. Developers do not need to wait for Operations to surface a problem; they can observe the same production signals directly. This compresses feedback loops, accelerates root-cause analysis, and reduces the coordination overhead that slows delivery in siloed organizations.

Smart Data: The Engine of Common Situational Awareness

Visibility at the level required by a mature DevOps organization depends on the quality and timeliness of the underlying data. One approach that has gained attention is the concept of smart data — metadata derived from processing and organizing wire data (network packet data) at the collection point, optimized for real-time analytics.

The distinction between smart data and traditional log-based monitoring is significant:

By analyzing every IP packet traversing the network in real time, smart data approaches can deliver condensed, actionable datasets to all teams simultaneously. QA, Development, and Operations can all monitor evolving leading indicators without waiting for log aggregation pipelines to catch up. This eliminates bottlenecks in the feedback loop and enables issues to be identified and addressed in real time — rather than discovered in a post-mortem.

Security: The DevSecOps Dimension

The importance of visibility becomes even more acute when security is considered as part of the picture — as it must be in a DevSecOps organization. In traditional security models, a breach is typically analyzed after the fact: logs are reviewed, forensic work is done, and a retrospective report is produced. This is valuable, but it is inherently reactive.

In a DevSecOps model, security engineers work alongside developers throughout the development lifecycle. Real-time visibility into which application behaviors are anomalous — or which known vulnerability patterns are being triggered — allows security findings to feed directly back into the development pipeline. Issues can be addressed at the source, before they compound.

When combined with automation, this approach can:

• Reduce the need for reactive incident response war rooms.
• Enable security defects to be caught and fixed earlier in the delivery cycle, when they are cheaper to address.
• Limit potential reputational damage by reducing the window between vulnerability introduction and remediation.

Putting It Together: Visibility as a Strategic Capability

Digital transformation and hybrid cloud adoption introduce genuine complexity. Applications span on-premises infrastructure, private cloud, and public cloud environments. Traffic flows are more varied and harder to instrument than in traditional data center deployments. This is precisely the environment in which visibility becomes a strategic differentiator rather than a nice-to-have.

Organizations that invest in common situational awareness — through real-time telemetry, cross-functional dashboards, and smart data pipelines — give their DevOps and DevSecOps teams the foundation they need to operate effectively. Siloes break down not through cultural mandates alone, but through shared tooling that makes collaboration the path of least resistance.

The organizations that will maintain a competitive edge in rapidly disrupted markets are those whose engineering teams can see the full picture, react quickly, and continuously improve — together. Mature DevOps practice is not a destination; it is an ongoing journey of improving telemetry, tightening feedback loops, and deepening the shared awareness that allows fast, safe, and secure delivery at scale.

Why do most internet of things (IoT) analytics operations occur in the cloud? The public cloud offers a centralised location for large amounts of affordable storage and computing power. But there are many instances in which it makes more sense to perform analytics closer to the thing or activity that is generating or collecting data ­– equipment deployed at customer sites.

This is particularly true in industrial and manufacturing environments, which are familiar with the challenges of managing massive amounts of unstructured data, but may lag when it comes to the virtualisation of IT infrastructure.

Industrial cloud market development

Advances in intelligent process manufacturing, factory automation, artificial intelligence and machine learning models all benefit from edge analytics implementations, yet will likely become islands of automation without a cohesive industrial cloud computing platform.

The industrial cloud covers everything from the factory floor to the industrial campus, and it is unifying the supply chain as companies employ a combination of digital business, product, manufacturing, asset, and logistics planning to streamline operations across both internal and external processes.

Industrial cloud applications make it easier to optimise asset and process allocations by modeling the physical world and use data and subsequent insights to enable new services or improve control over environmental, health, and safety issues.

The virtualisation of business-critical infrastructure is transforming the production and distribution of goods and services throughout the supply chain, as industrial organisations shift focus to hybrid cloud computing deployments that connect and integrate on-premise IT resources with public cloud resources.

According to the latest worldwide market study by ABI Research, hybrid industrial cloud adoption will more than double over the next five years at a respectable 21.1 percent CAGR.

Initial IoT deployments in industrial markets reflect the sector’s machine to machine (M2M) heritage: private cloud infrastructure as a service (IaaS). The private IaaS model served as a solid starting point for many organisations that wanted the benefit of cloud scale, but with minimal interruption to normal IT operations.

The industrial cloud platform as a service (PaaS) model extended the functional capabilities of on-premise IaaS solutions by shifting commodity tasks – such as capacity planning, software maintenance, patching — to public cloud service providers. Software as a service (SaaS) took it a step further but in the form of managed services.

“Manufacturing and industrial organisations were not born from the same digital core as the people they employ or the products they produce,” said Ryan Martin, principal analyst at ABI Research. “But they also harness some of the greatest potential thanks to massive amounts of untapped plant and process log data. Harvested with the right analytical tools and guidance, these data streams can deliver value greater than the sum of their parts.”

The factory floor’s historical predisposition toward on-premise solutions has been supplanted by a campus-led approach underscored by a more recent push to connect HMI, SCADA, and control networks to higher-level enterprise systems, as well as the public cloud.

However, getting to the point where all these moving pieces come together in a real-world, production environment can be messy. Many operational technology (OT) devices come up short in key areas such as interoperability and security due to the prevalence of proprietary protocols in the legacy M2M market.

Outlook for industrial cloud app development

“Most OT systems depend on infrastructure with lifetimes measured in decades, while IT systems can be upgraded frequently at little or no cost,” concludes Martin.

As a result, industrial and manufacturing markets typically employ a staged technology integration strategy that favors suppliers whose hardware, software, and services can be acquired incrementally, with minimal disruption to existing operations. The hybrid IT infrastructure models can fit very well in this operational environment.