Why Cloud Computing Matters in Healthcare

Healthcare generates enormous volumes of data — clinical records, diagnostic imaging, genomics, IoT sensor feeds from wearables and medical devices, and administrative data. On-premises infrastructure struggles to scale cost-effectively to meet these demands. Cloud platforms offer elastic compute and storage, global redundancy, and managed services that let healthcare engineering teams focus on application logic rather than data center operations.

Key drivers pushing healthcare organizations toward cloud adoption include:

• Regulatory modernization: Frameworks such as HIPAA (US), GDPR (EU), and similar regional regulations have matured to explicitly address cloud-hosted protected health information (PHI), giving compliance teams clearer guidance for cloud deployments.
• Telehealth expansion: The rapid growth of remote patient monitoring and virtual consultations requires scalable, low-latency infrastructure that cloud platforms are well positioned to provide.
• Interoperability mandates: Standards such as HL7 FHIR are pushing healthcare systems to expose APIs, which fits naturally into cloud-native microservices architectures.
• Cost pressure: Moving from capital expenditure (CapEx) on hardware to operational expenditure (OpEx) on cloud services appeals to hospital CFOs facing tight margins.
• AI and machine learning workloads: Training models on medical imaging, clinical notes, and genomics data requires GPU clusters that are far more accessible on-demand via cloud providers than through owned hardware.

Cloud Computing Stack Layers in Healthcare

Healthcare cloud deployments typically span all three classic cloud service models, often used together:

Infrastructure as a Service (IaaS)

IaaS gives healthcare organizations raw compute, storage, and networking. Common use cases include disaster recovery replication of on-premises EHR databases, PACS (Picture Archiving and Communication System) storage for DICOM imaging files, and lift-and-shift migrations of legacy clinical applications.

Platform as a Service (PaaS)

PaaS abstracts infrastructure management and is attractive for healthcare software vendors building SaaS products. Managed Kubernetes services, serverless functions, managed databases, and FHIR-native API services (such as Google Cloud Healthcare API and Azure Health Data Services) fall into this category.

Software as a Service (SaaS)

SaaS dominates end-user adoption in healthcare. EHR platforms, revenue cycle management tools, telehealth applications, and population health management systems are increasingly delivered as SaaS, reducing the operational burden on hospital IT departments.

Deployment Models

Healthcare organizations rarely adopt a single deployment model. The most common patterns are:

• Public cloud: Workloads run on shared infrastructure managed by providers such as AWS, Microsoft Azure, or Google Cloud. Suitable for non-sensitive analytics, dev/test environments, and SaaS applications with business associate agreements (BAAs) in place.
• Private cloud: Dedicated infrastructure, either on-premises or hosted, giving organizations maximum control over PHI. Common among large health systems with significant existing capital investment.
• Hybrid cloud: The dominant real-world architecture. Sensitive clinical data and latency-sensitive workloads remain on-premises or in private cloud, while burst capacity, analytics, and SaaS integrations run in the public cloud.
• Community cloud: Shared infrastructure among a consortium of healthcare organizations — for example, a regional health information exchange (HIE) operated jointly by member hospitals.

Market Segmentation by Application

Cloud computing in healthcare addresses a broad range of functional areas. The major application segments include:

• Clinical information systems: EHR, EMR, and clinical decision support platforms.
• Revenue cycle management (RCM): Billing, claims processing, and payer integration.
• Pharmacy management: Drug inventory, prescription processing, and clinical pharmacology tools.
• Medical imaging and PACS: Cloud-based storage and AI-assisted analysis of radiology images.
• Telehealth and remote monitoring: Video consultation platforms and IoT device data aggregation.
• Health analytics and population health: Large-scale data warehousing and BI dashboards.
• Healthcare IoT: Connected medical devices, wearables, and real-time patient monitoring streams.

Regional Landscape

Adoption rates and growth patterns vary significantly by region, shaped by regulatory environments, existing IT infrastructure maturity, and healthcare funding models:

• North America: The most mature market, driven by large integrated delivery networks, strong venture investment in health tech, and detailed HIPAA-era compliance frameworks that cloud providers have built around.
• Europe: Growth is significant but shaped by GDPR data residency requirements, which have driven major cloud providers to build regional data centers and offer data sovereignty guarantees.
• Asia-Pacific: Among the fastest-growing regions, led by government-driven digitization programs in countries such as India, China, Australia, and Singapore, combined with large underserved patient populations creating demand for scalable digital health infrastructure.
• Latin America, Middle East, and Africa: Earlier-stage markets where cloud adoption is accelerating, often leapfrogging legacy on-premises investments and moving directly to cloud-first or mobile-first healthcare delivery models.

Competitive Landscape

The market involves multiple tiers of players:

• Hyperscale cloud providers: AWS, Microsoft Azure, and Google Cloud all offer HIPAA-eligible services and have published healthcare-specific reference architectures. Each has pursued strategic partnerships with major EHR vendors and health systems.
• Healthcare-specific cloud vendors: Specialized platforms focus on FHIR-native data services, clinical data warehousing, or specific verticals such as genomics or medical imaging.
• EHR and clinical software vendors: Companies such as Epic, Oracle Health (formerly Cerner), and Meditech increasingly offer or mandate cloud-hosted versions of their platforms.
• System integrators and managed service providers: Organizations that architect, migrate, and operate healthcare cloud environments, particularly important for health systems that lack deep cloud engineering capability internally.

Key Technical Considerations for Engineering Teams

For DevOps and platform engineers working on healthcare cloud systems, several concerns deserve particular attention:

• Business Associate Agreements (BAAs): Every cloud service that processes PHI requires a signed BAA with the provider. Not all services within a cloud platform are covered — verify which specific services are eligible before using them in a PHI context.
• Encryption in transit and at rest: Non-negotiable for PHI. Use TLS 1.2 or later for data in transit and provider-managed or customer-managed keys (CMK) for storage encryption.
• Audit logging: HIPAA requires audit trails for access to PHI. Ensure CloudTrail, Azure Monitor, or equivalent services are enabled and logs are tamper-evident and retained per policy.
• Network segmentation: PHI workloads should be isolated in dedicated VPCs or VNets with strict security group and firewall rules. Avoid co-mingling PHI and non-PHI workloads without appropriate controls.
• Disaster recovery and RTO/RPO: Healthcare applications often have stringent uptime requirements. Define and test recovery time objectives (RTO) and recovery point objectives (RPO) for all critical systems.
• FHIR API security: Implement OAuth 2.0 with SMART on FHIR scopes for any FHIR-based API exposure, and conduct regular penetration testing.

Conclusion

Cloud computing in healthcare is not a future trend — it is the present reality for a growing majority of health systems, payers, and digital health companies. For engineering and DevOps teams operating in this space, understanding the market structure helps contextualize technology decisions: which platforms are investing heavily in healthcare capabilities, which compliance frameworks govern deployments, and which application areas are seeing the most demand. The fundamentals of secure, compliant, and resilient cloud architecture remain constant, but the healthcare context adds a layer of regulatory and safety-critical nuance that makes rigorous engineering practice non-negotiable.

Overview

Origin Protocol has partnered with Google Cloud to bring its decentralised commerce platform — branded Dshop — to the Google Cloud Marketplace. The move gives Google Cloud’s global network of developers and merchants a straightforward path to deploying blockchain-powered e-commerce stores. The partnership also includes a data integration that surfaces publicly available on-chain product data inside Google BigQuery, making it easier to analyse and build on top of.

What Is Origin Dshop?

Dshop is Origin Protocol’s open-source, decentralised storefront product. Each store is built on two decentralised technologies:

• Ethereum — acts as the authoritative, tamper-resistant ledger for product listing data and transactions.
• IPFS (InterPlanetary File System) — stores and distributes store content in a censorship-resistant, peer-to-peer manner.

Key characteristics of a Dshop include:

• Fully customisable — merchants control the full seller experience and branding.
• Censorship-resistant — because data lives on-chain and on IPFS, no single authority can take a store down.
• Free to use — there are no platform fees charged by Origin for running a store.
• Flexible payments — stores accept Ethereum (ETH), ERC-20 tokens (including Origin’s own OGN token), traditional credit cards, and PayPal.
• Drop-shipping integrations — native connections to select drop-shipping providers simplify order fulfilment.

Google Cloud Marketplace Listing

By listing on the Google Cloud Marketplace, Origin makes it possible for merchants and developers already operating within the Google Cloud ecosystem to discover, deploy, and operate a Dshop without leaving a familiar environment. As a formal Google Cloud Partner, Origin will co-market the platform to a wider range of merchant verticals, including:

• Physical e-commerce goods
• Digital goods
• Rental marketplaces
• Service-based businesses

Origin co-founder Matthew Liu described the vision in a company blog post:

“We envision a future where thousands of Dshops across many different verticals (e-commerce, digital goods, rentals, other services) are powered by the Origin technology platform running on Google Cloud infrastructure. We look forward to working with the Google Cloud team to bring new technology integrations to the market in the near future.”

Matthew Liu, Co-founder, Origin Protocol

Google BigQuery Integration: Making Blockchain Data Queryable

One of the most technically significant parts of this partnership is the decision to pipe publicly available Dshop product data into Google BigQuery. This addresses a longstanding practical problem: blockchain data is inherently difficult to index and query at scale.

Because merchants who opt into publishing their listing data on-chain make that data fully public, anyone can read it in principle — but extracting meaningful insights from raw blockchain data requires significant tooling. Origin uses the Ethereum ETL (Extract, Transform, Load) library, a project that Google Cloud participates in, to extract on-chain data and load it into BigQuery’s familiar SQL-queryable environment.

The practical result is something analogous to applying a search and indexing layer on top of raw blockchain data. Once the data is in BigQuery, three groups of users benefit directly:

• Affiliates and marketers — can discover products across Origin’s merchant network to promote, driving additional sales without needing to navigate raw blockchain explorers.
• Data scientists — can write standard SQL queries to analyse which merchant categories and product types are growing on-chain, identify trends, and produce reports.
• Developers — can query the shared public data layer and build third-party applications — recommendation engines, price trackers, analytics dashboards — on top of live, verified product data.

Why This Matters for the Decentralised Commerce Stack

Pairing a decentralised storefront with a managed cloud hyperscaler’s data warehouse illustrates a pragmatic architectural pattern: use decentralised infrastructure (Ethereum, IPFS) for the properties it does well — censorship resistance, data integrity, trustless settlement — while routing analytics workloads through centralised, highly optimised tools (BigQuery) where speed and query flexibility matter most.

For DevOps and platform engineering teams evaluating Web3 commerce stacks, this integration is worth noting because it reduces the operational burden of building custom blockchain indexers and ETL pipelines from scratch. Leveraging Ethereum ETL and BigQuery as managed infrastructure lowers the barrier to delivering analytics on on-chain data.

Key Takeaways

• Origin Dshop is now listed on the Google Cloud Marketplace, simplifying deployment for merchants and developers in the Google Cloud ecosystem.
• Publicly available on-chain product data from Dshop merchants is queryable via Google BigQuery using the Ethereum ETL library.
• Dshops support crypto payments (ETH, ERC-20/OGN), credit cards, and PayPal, with drop-shipping provider integrations built in.
• The architecture demonstrates a hybrid pattern: decentralised data storage and settlement, centralised analytics and discoverability.

This article is based on reporting from channellife.com and an Origin Protocol blog post published in August 2020.

From Bespoke Muskets to Interchangeable Software

In 1801, Eli Whitney demonstrated the military value of interchangeable parts to President John Adams and President-elect Thomas Jefferson by disassembling several muskets and reassembling one working firearm from randomly selected parts. Before Whitney’s demonstration, muskets were bespoke, artisanal creations — each component custom-fitted to a specific weapon. Replacing a single part required a skilled gunsmith.

Today, interchangeable parts are taken for granted across every domain of manufacturing, from rifle bolt carriers to vehicle alternators. Yet government information systems frequently suffer from the same problem Whitney solved more than two centuries ago: they are bespoke, tightly coupled constructions that require specialist teams to configure, deploy, modify, or repair when something breaks.

To address that problem, the Department of Defense (DOD) has been accelerating adoption of DevSecOps — an approach that aims to bring the same standardization, repeatability, and resilience to software that interchangeable parts brought to manufacturing.

What Is DevSecOps?

DevSecOps combines three disciplines — Development, Security, and Operations — into a unified culture supported by integrated processes and tooling. The goal is to enable more efficient delivery and ongoing management of secure software at pace.

The critical distinction between DevSecOps and conventional DevOps is the treatment of security. In traditional software development processes — including many Waterfall-era models — security is a checkpoint: a gate the project must pass before release. In DevSecOps, security is a first-class citizen woven into every stage of the lifecycle, from initial design and code review through automated testing, deployment, and post-release monitoring.

Federal adoption is broad. Agencies including the General Services Administration (GSA), the U.S. Air Force, the U.S. Army, and the U.S. Navy have active DevSecOps programs.

The Three Pillars of DevSecOps

1. Processes

From a process perspective, DevSecOps extends beyond the software development lifecycle practices of agile and Continuous Integration/Continuous Delivery (CI/CD) to encompass operational behaviors of the running system — including security posture.

A key enabler is configuration as code: expressing infrastructure and environment configuration in version-controlled files rather than through manual, undocumented procedures. When operational tasks are defined as code, they can be automated, reviewed, tested, and repeated reliably. The practical result is push-button automation — the ability to redeploy an entire infrastructure component, from bare metal to full operational capability, by triggering the appropriate automation playbook. That kind of repeatability dramatically increases system resilience and reduces mean time to recovery.

Iterative delivery also shifts the risk profile of a project. Rather than investing months or years before any working software reaches users, teams release smaller increments, gather real-world feedback, and course-correct continuously. This is the principle of moving risk left — catching problems early, when they are cheapest to fix.

2. Tools

No single product constitutes a DevSecOps platform. There is no “DevSecOps box” you can install in a data center. Successful programs assemble a toolchain covering several functional categories:

• Source code version control — tracking changes and enabling collaboration (e.g., Git-based platforms)
• Build automation — compiling, packaging, and publishing software artifacts on every commit
• Test automation — unit, integration, and end-to-end tests that run without human intervention
• Security validation — static analysis (SAST), dynamic analysis (DAST), dependency scanning, and container image scanning integrated into the pipeline
• Performance testing — load and stress testing to validate that deployments meet operational requirements
• Configuration management — tools such as Ansible, Terraform, or similar that codify infrastructure state
• Project management — issue tracking, backlog prioritization, and team collaboration tools

The emphasis should be on selecting the right classes of tools for your organization’s mission and maturity, rather than replicating another organization’s exact stack. Tooling choices will vary across agencies, classification environments, and mission sets.

3. People and Organizational Culture

The people dimension is often the most challenging aspect of DevSecOps adoption in the federal government. Conway’s Law observes that organizations tend to produce system designs that mirror their own communication structures. Agencies organized into functional silos — separate development, security, and operations branches that communicate primarily through formal ticketing systems — will naturally produce siloed, tightly coupled systems.

DevSecOps requires a different structure. Effective adoption calls for cross-functional product teams in which developers, security engineers, and operations staff work together continuously toward a shared goal: successfully shipping and sustaining a product. Many government agencies are actively flattening hierarchies and reorganizing personnel around products rather than around technical roles.

This restructuring drives results-oriented outcomes. When everyone on a team shares accountability for the security, reliability, and delivery of the same product, the incentives align and velocity improves.

Key Advantages of DevSecOps for Government IT

Shorter Time to Value

Traditional Waterfall development front-loads extensive requirements documentation and design before a single line of production code is written. This approach can lock teams into delivering a large feature set at the first release — features that may not all deliver meaningful value to end users. By contrast, DevSecOps’s iterative feedback loop allows agencies to release smaller, higher-impact increments and measure their effect before committing to the next set of capabilities. The result is a shorter gap between investment and operational value.

Faster Fielding of New Capabilities

Automated pipelines reduce the manual effort required to build, test, and deploy software. With security validation integrated into automation rather than handled as a separate manual review, the time between writing code and having it running in production shrinks substantially. For the DOD, this means new warfighting capabilities or mission-critical tools can reach users in weeks rather than years.

Moving Risk Left

Integrating security testing, compliance checks, and vulnerability scanning into the CI/CD pipeline means defects are found when they are introduced — not months later during a pre-release security audit. Catching a vulnerability during development is orders of magnitude cheaper to fix than discovering it in a production system under operational load. This “shift-left” approach to security is one of the most concrete risk-management benefits DevSecOps delivers.

Increased Resilience Through Automation

When infrastructure configuration is expressed as code and deployments are fully automated, recovery from failures becomes a repeatable procedure rather than a heroic manual effort. The same automation that performs a routine deployment can restore a failed system to a known-good state — reducing downtime and the dependence on individual specialist knowledge that makes bespoke systems so fragile.

Conclusion

Whitney’s interchangeable parts made it possible for soldiers in the field to maintain their own weapons without a gunsmith. DevSecOps pursues an analogous goal for government information systems: standardized, automated, and resilient platforms that can be maintained, extended, and recovered by cross-functional teams rather than bespoke specialist configurations. With broad adoption underway across the DOD and the wider federal government, DevSecOps is no longer an experimental approach — it is becoming the operational baseline for modern government IT delivery.

Overview

Hewlett Packard Enterprise (HPE) extended its container platform — rebranded as HPE Ezmeral — to support a data fabric that spans multiple Kubernetes clusters distributed across geographically separated data centers. The announcement centers on HPE Ezmeral Data Fabric 6.2, a release that adds significant capabilities for enterprises building large-scale, distributed containerized workloads, particularly those involving machine learning.

Background: MapR Acquisition and the Ezmeral Brand

The data fabric technology at the core of HPE Ezmeral originates from HPE’s acquisition of MapR Technologies. MapR had long offered a distributed data platform with strong support for files, tables, and event streams over a unified namespace. After the acquisition, HPE integrated this technology into its broader container and data management portfolio and rebranded the platform under the Ezmeral name.

What HPE Ezmeral Data Fabric Does

At its core, the HPE Ezmeral Data Fabric creates a global namespace accessible through standard APIs. This means applications — whether containerized or non-containerized — can read and write data without needing to know the physical location of that data. Data can be stored and accessed as:

• Files — using a POSIX-compatible file system interface
• Tables — for structured data with NoSQL-style access
• Event streams — for real-time, pub/sub messaging patterns

A data mirroring capability enables movement of data within or between clusters. This uses bi-directional, multi-master table replication and event stream replication, making it suitable for active-active distributed deployments where data consistency across sites is required.

New Features in HPE Ezmeral Data Fabric 6.2

According to Nanda Vijaydev, distinguished technologist and lead data scientist at HPE, version 6.2 enables IT organizations to manage fleets of Kubernetes clusters spread across multiple geographic locations. The specific new capabilities in this release include:

• Snapshot restore — the ability to roll back data volumes to a previous point-in-time state
• Fine-grained data placement — allows administrators to pin or tier data to specific classes of heterogeneous storage devices within and across nodes
• Policy-based security — enforces data governance rules automatically, reducing the burden on individual application developers to implement access controls
• Last access tracking — provides visibility into when data was last read, useful for lifecycle management and cost optimization
• Flash storage enhancements — improved performance and reliability when using NVMe or SSD-backed storage tiers
• Global file check enhancements — improved consistency verification across distributed file system volumes
• Improved erasure coding performance and rebuild — faster recovery of data after node or drive failures without full replication overhead
• Better management metrics — expanded observability for administrators monitoring cluster health and data operations
• External key management support — integration with external KMS (Key Management Service) systems for enterprise encryption workflows

Architecture: A Stackable Platform on Kubernetes

Vijaydev describes the HPE Ezmeral platform as a stackable services model built on top of a core Kubernetes cluster. Rather than deploying a single monolithic platform, IT teams can add capabilities incrementally as their container application environment matures. The data fabric layer provides the persistent, distributed storage foundation, while additional services — including ML tooling and cluster management — can be layered on top.

This approach is intended to lower the initial barrier to adoption while giving organizations a clear upgrade path as workload complexity grows.

MLOps as a Primary Driver

A significant portion of HPE’s positioning around Ezmeral targets machine learning operations (MLOps). Training large machine learning models requires access to massive datasets. Containers make it practical to decompose what would otherwise be monolithic training pipelines into modular, independently updatable components. Managing training data, model artifacts, and inference results across multiple clusters and locations requires exactly the kind of distributed, consistent data layer that Ezmeral aims to provide.

HPE’s view is that as ML workloads scale, enterprises will need a cohesive software stack addressing both the Kubernetes management layer and the data management layer — rather than assembling those capabilities from disparate open-source tools.

Deployment Options: Self-Managed vs. GreenLake

HPE offers the Ezmeral platform through two routes:

• Self-managed — the organization’s internal IT team deploys and operates the platform on their own infrastructure
• HPE GreenLake — a managed service model in which HPE handles infrastructure operations and, where applicable, updates to select third-party packaged application environments

The GreenLake option reflects a broader industry trend toward consumption-based IT, where enterprises prefer to pay for outcomes rather than own and operate infrastructure outright. HPE is positioned to benefit if organizations decide to offload Kubernetes and data fabric management as those environments grow in complexity.

Implications for Enterprise Kubernetes Strategy

Running Kubernetes in a single cluster is operationally challenging enough. Running fleets of clusters across multiple data centers — with shared, consistent data — raises the complexity significantly. Capabilities like global namespaces, bi-directional replication, and policy-based governance directly address the pain points that arise at that scale.

For DevOps and platform engineering teams evaluating multi-cluster data strategies, the key questions to assess against any solution — including HPE Ezmeral — include:

• How does the platform handle network partitions between geographically separated clusters?
• What is the replication latency for event streams and tables under realistic WAN conditions?
• How does policy-based security integrate with existing identity providers (e.g., LDAP, Active Directory, OIDC)?
• What is the operational overhead of running the data fabric control plane alongside existing Kubernetes operators?

HPE Ezmeral Data Fabric 6.2 represents a meaningful step toward making geo-distributed Kubernetes data management a production-ready capability, with MLOps use cases providing the commercial urgency to accelerate development.

What Is Datadog Incident Management?

Datadog announced the general availability of Incident Management, a product designed to consolidate on-call response workflows for DevOps and security teams. Rather than juggling multiple disconnected tools for alerting, communication, tracking, and investigation, engineers can now work from a single, unified interface that brings together alerting data, documentation, and real-time collaboration.

The goal is straightforward: reduce the time wasted querying disparate systems during an active incident, and make it easier to onboard additional responders quickly when an outage escalates.

The Problem: Fragmented Tooling During Incidents

When a performance or security incident occurs, every minute of downtime increases the number of affected users and transactions — and the potential revenue and reputational damage to the business. Yet on-call engineers have historically had to coordinate across a patchwork of tools:

• Alerting platforms to detect the issue
• Chat tools (such as Slack) to coordinate the response team
• Ticketing systems to track tasks and ownership
• Wikis or runbooks for documentation and procedures
• Monitoring dashboards to investigate root cause

Context-switching between these tools slows the mean time to resolution (MTTR) and complicates post-incident review, since relevant data is scattered across systems.

“Managing incidents across disparate tools for alerting, communicating, tracking, and investigating makes it difficult to both solve a problem while it’s happening and conduct a post-mortem to prevent it from happening again.”

Marc Weisman, Vice President of Product, Datadog

Core Capabilities of Datadog Incident Management

Datadog Incident Management centralizes the incident lifecycle in one place. Key capabilities at launch included:

Incident Declaration from Alerts and Graphs

Engineers can collect signals from across Datadog data sources and declare an incident directly from an anomalous graph or a triggered alert. This eliminates the manual step of copying context into a separate incident tracking tool.

Incident Timeline and Task Tracking

Responders can document a chronological timeline of events as the incident unfolds, and log follow-up tasks within the same interface. This structured record becomes the foundation for post-mortem analysis once the incident is resolved.

Incident History for Post-Mortems

All collaboration and data associated with an incident is automatically saved in an incident history. Teams can return to this record to conduct thorough post-mortem reviews and identify patterns that could prevent future occurrences.

Companion Features Released Alongside Incident Management

Datadog released several additional capabilities to complement the Incident Management workflow:

Mobile App (Android and iOS)

A native mobile application — reaching general availability at the same time — allows engineers to interact with Datadog monitors and dashboards from their phones. This is particularly valuable for on-call engineers who need situational awareness outside of normal working hours.

Slack ChatBot Integration

A Datadog ChatBot for Slack allows teams to manage incidents and query Datadog data without leaving their primary communication channel. This supports chat-driven incident response workflows that many DevOps teams already rely on.

Collaborative Notebooks

Datadog Notebooks received updates enabling real-time collaboration, including presence detection (showing who else is viewing or editing the document) and live content updates without requiring a page refresh. This makes notebooks a viable tool for live incident documentation shared across a distributed response team.

Customer and Partner Perspectives

“At Olo it’s critical to always have our finger on the pulse of our systems in order to keep restaurants up and running. While Datadog is already an important part of that, the release of integrated Incident Management can further improve our team’s ability to respond quickly and effectively, in those times it matters most.”

Greg Shackles, Vice President of Technology, Olo

“effx provides users the context and contributing factors of an incident in real-time. We’re excited to partner with Datadog for Incident Management to provide further insight into managing incidents that can potentially impact just one or thousands of microservices.”

Joey Parsons, Founder & CEO, effx

Why Incident Management Matters for DevOps Teams

Incident management is not just a reactive capability — it directly affects business outcomes. A unified incident platform supports several DevOps best practices:

• Reduced MTTR: Faster access to correlated data means engineers spend less time hunting for information and more time fixing the problem.
• Better post-mortems: An automatically generated incident timeline provides an accurate record of events, reducing the effort required to reconstruct what happened.
• Scalable collaboration: New team members joining a live incident can quickly get up to speed from a single pane of glass rather than being briefed across multiple tools.
• Continuous improvement: Structured incident histories enable teams to identify recurring failure modes and prioritize reliability investments accordingly.

Industry Context

The launch coincided with a period of significantly increased pressure on digital infrastructure. As organizations accelerated their shift to digital channels, the business impact of performance incidents grew accordingly. Analyst firm 451 Research noted at the time that businesses would increasingly rely on monitoring, incident management, and collaboration tools to resolve performance problems before they cause customer and revenue loss.

Datadog Incident Management positions the company to serve as a more complete operational platform — moving beyond observability into the workflow layer that determines how quickly teams can act on what they observe.

Summary

Datadog Incident Management centralizes alerting signals, documentation, timelines, and team collaboration into a single platform, reducing the friction of multi-tool incident response. Combined with a mobile app, a Slack ChatBot, and collaborative notebooks, the product suite addresses the full lifecycle of a DevOps incident — from detection through resolution and post-mortem review.

For DevOps teams operating under SLA pressure with distributed on-call rotations, consolidating this workflow can meaningfully reduce both MTTR and the cognitive overhead of incident response.

Why Content Teams Are Turning to Agile and DevOps Principles

At the core of Agile methodology is a focus on team coordination over rigid process. That same principle applies directly to content production. Bob Gower, an Agile marketing coach and co-author of Radical Alignment, frames it clearly: “As with DevOps and Agile, successful content management in my experience is less about technology and more about workflows and teamwork. The power of a flexible workflow is the ability to reflect and get better continuously. Teams and management need to embrace this, but it can feel a tad chaotic at times.”

The parallel to software delivery is intentional. In DevOps, teams break work into short cycles, automate repetitive steps, and continuously improve based on feedback. Content teams adopting similar practices replace waterfall-style handoffs — one person writes, sends to an editor, who sends to an approver — with parallel, iterative workflows where multiple contributors can work on the same asset simultaneously.

According to Nucleus Research analyst Evelyn McMullen, collaboration tools alone are not enough. AI, machine learning, and metadata will be key for streamlining processes and improving media workflow management. Before investing in any tooling, she recommends that organizations first map their workflows, define the outcomes they need to manage, and build a strategy. “One challenge we see enterprises running into is immediate need for content management without any planning or strategy in place,” she said.

The Baseline: Moving Beyond Email Attachments and Word Documents

The simplest starting point for collaborative content work is a non-destructive, multi-user editor. Tools like Google Docs eliminate the need for version control via email, ensure no edits are lost, and allow multiple contributors to work in parallel. Gower is blunt about the alternative: “I’m shocked at how many editors still send around Microsoft Word docs. It’s so inefficient.”

From there, project-specific tools can automate handoffs and task assignments. The gap between content management maturity and software development maturity remains significant, however. As Dan Stoll, director of technical product marketing at workflow automation provider Nintex, puts it: “Content management normally is a set-and-forget process in that people add content, content goes for review and content gets published.” DevOps, by contrast, is characterized by short activity bursts, shared libraries, and reusable components. Stoll believes content tooling will eventually reach similar automation depth, but organizations will need purpose-built tools in the interim.

Real-World Example: Automating Content Cross-Pollination at Nintex

Nintex has built an internal content platform that demonstrates what automated content pipelines can look like. Sales teams can locate marketing materials and price books based on criteria already configured in the CRM system. A cloud automation service reviews and moves approved content from a central gallery into the sales team’s platform automatically. “This process is completely automated,” Stoll explained, “and allows us to go about publishing and reviewing as normal in the gallery, knowing that our content is being cross-pollinated into other systems to increase visibility.”

AI and Metadata: The Next Layer of Media Workflow Intelligence

AI is beginning to take on meaningful roles in content production workflows. Current applications include:

• Automated metadata generation for video and audio assets, enabling fast search, retrieval, and repurposing
• Content scanning for relevancy, flagging outdated material for revision
• PII redaction — automatically identifying and removing sensitive data such as phone numbers or credit card details before publication
• Language identification, entity extraction, sentiment analysis, and scene detection via low-code AI integrations

Greg Loose, head of media and entertainment at AI technology provider Veritone, sees AI-generated metadata as a practical, near-term win: it allows companies to locate media assets within seconds and quickly share, repurpose, license, and monetize content. “AI is interwoven within other enabling technologies, such as on-demand media asset management platforms, making them and the assets themselves intelligent,” he said.

Case Study: Recovering from a Content Database Disaster at WikiLawn

WikiLawn, an online lawn and garden resource, lost its entire content database due to data corruption. The incident forced a complete overhaul of the organization’s content tools and processes, according to president Dan Bailey. The existing on-premises enterprise content management (ECM) system made searches difficult and slowed imports from the website and social media channels.

The team migrated to Microsoft SharePoint as a combined content hub and management service. The transition — including assessing storage needs, evaluating existing content, and completing the move — took approximately two weeks. The database can now operate in the cloud or on-premises, with fast storage, categorization, and search capabilities.

Bailey remains candid about the limitations: “Content management services are focused more on access right now than seamless iteration, which is one of the main things we need.” His ideal would be an all-in-one platform supporting multiple content types with automatic saving to both online and local branches — effectively a content equivalent of a Git-backed workflow.

Case Study: Iterating on Content Like Code at SEO.co

Nate Nead, CEO of digital marketing firm SEO.co, draws a direct analogy between software development and content production: “Getting to the best version of a piece of content is like iterating to the best version of a piece of code.” The company runs weekly content scrum meetings to assign tasks and track progress, having migrated from spreadsheets and Google Docs to Asana for task management and Slack for communications.

“Being nimble is about having the right process coupled with the right tools,” Nead said. The structured sprint cadence ensures content moves through planning, drafting, review, and publication without accumulating in someone’s inbox.

COVID-19 as an Accelerant for Collaborative Content Tooling

Remote work mandates in 2020 forced organizations that had been evaluating collaborative content tools to deploy them immediately. Michael Segner, director of content and communications at AvePoint — a provider of migration, backup, and governance software for Microsoft 365 — reported a sharp spike in adoption of Microsoft Teams and related tools as organizations sent digital workers home.

Segner’s own team moved away from a waterfall-style linear process — write, email, edit, email, approve — to a channel-based model in Teams. Documents are uploaded to a specific channel where any authorized team member can rewrite and edit in parallel. Once approved, the document is published to a central “Go To Market” SharePoint site for internal or public access. “This way, we have a single authoritative document that our whole company can access and leverage and that is easy to iterate,” Segner noted.

What to Call This: The Naming Problem in Content Workflow Evolution

Software delivery has two widely recognized terms — DevOps and Agile — to describe faster, more collaborative, iterative lifecycles. Content teams lack an equivalent shorthand. Several terms have been proposed by analysts and practitioners:

• New work nucleus (Gartner): A framework in which content collaboration platforms replace enterprise file sync and share (EFSS), workstream collaboration replaces unified communications, and collaborative work management replaces traditional project management tools
• Intelligent information management (Nucleus Research): Extends beyond traditional content services to encompass AI-driven classification and retrieval
• Content automation (Nintex): The use of AI to move content through production and distribution phases
• Intelligent content (Veritone): All content supported by AI through metadata and workflows to drive engagement and revenue
• Content as a service (FileCloud): Enabled by metadata integration, smart classification, powerful search, flexible APIs, and cloud-agnostic open systems
• Cloud content management (Box): Encompasses file organization, sharing, storage, access control, permission management, and collaborative workflow

No single term has achieved consensus, but the underlying direction is consistent: content workflows are moving toward the same principles that define modern software delivery — automation, iteration, shared ownership, and continuous improvement.

Case Study: Agile Content Refresh at Simple Life Insure

Insurance broker Simple Life Insure in San Diego needed to keep policy and underwriting documents current while also refreshing website content to maintain search performance. CEO Ty Stewart opted for SaaS tools rather than custom software. The team’s content workflow uses:

• BuzzSumo — for identifying trending topics and emerging long-tail keywords, and for surfacing existing content that needs to be restructured
• Trello — for managing workflow stages: finding topics, drafting SEO-optimized outlines, writing, reviewing, publishing, and monitoring performance
• Cloud-based project management — to move tasks between team members and track completion

“In our industry, making those timely updates is pivotal since information needs to be accurate, given changing regulations in the finance and insurance world, which happens annually,” Stewart said. He acknowledges the workflow doesn’t perfectly mirror DevOps or Agile practices for code, but adds: “They absolutely work for our content refresh strategies and only require a cloud-based project management tool to keep everything on track.”

Key Takeaways for DevOps and Engineering Teams Supporting Content Workflows

• Start with process, not tooling. Map your workflows and define your outcomes before evaluating platforms. Technology amplifies good process and amplifies bad process equally.
• Eliminate linear handoffs. Waterfall-style sequential editing creates versioning problems and delays. Channel-based or document-based parallel editing removes these bottlenecks.
• Invest in metadata. AI-generated metadata is the foundation for fast asset retrieval, content reuse, and monetization — particularly for video and audio.
• Treat content iteration like code iteration. Weekly scrums, task tracking in project management tools, and defined review stages bring software delivery discipline to content teams.
• Automate cross-system content distribution. Publishing to one system and manually copying to others is the content equivalent of deploying by hand. Automation pipelines that push approved content across systems reduce errors and save time.
• Plan for cloud-native or hybrid storage. On-premises ECM systems create search bottlenecks and import delays. Cloud or hybrid architectures support faster collaboration and disaster recovery.

The convergence of DevOps thinking and content management is still early. The tooling is maturing, the terminology is unsettled, and the automation depth lags well behind what software teams take for granted. But the direction is clear: content workflows that embrace iteration, automation, and shared ownership will outperform those that don’t.

This article is based on reporting originally published at searchcontentmanagement.techtarget.com.

1. Source Code Management (SCM) Tools

Source code management is the foundation of any repeatable software delivery process. A well-configured SCM system gives every member of a distributed engineering team a single source of truth for application code, configuration files, and infrastructure definitions.

Key capabilities to look for in an enterprise SCM tool include:

• Branching and merging strategies — support for Git Flow, trunk-based development, or feature-flag workflows so teams can work in parallel without conflicts.
• Version history and rollback — the ability to compare any two commits and revert to a known-good state reduces the blast radius of bad releases.
• Access controls and audit logs — fine-grained permissions protect sensitive code and satisfy compliance requirements by tracking who changed what and when.
• Repository hosting and mirroring — self-hosted or cloud-hosted options (GitHub Enterprise, GitLab, Bitbucket Data Center) that integrate with the rest of the toolchain.

When SCM is tightly integrated with CI/CD pipelines and code review workflows, it becomes the control plane for the entire software delivery lifecycle rather than just a backup mechanism.

2. Containerization Platforms

Containers solve one of the oldest enterprise software problems: environmental inconsistency between development, staging, and production. By packaging an application together with all of its dependencies into a portable, immutable image, containers make "it works on my machine" arguments largely irrelevant.

Two tools dominate this space:

• Docker — the de facto standard for building and running container images locally and in CI pipelines.
• Kubernetes — the leading container orchestration platform for scheduling, scaling, and managing containerized workloads across clusters in production.

Adopting containers also facilitates a microservices architecture. Large monolithic applications can be decomposed into independently deployable services, each versioned and scaled on its own schedule. Before committing to an orchestration platform, evaluate your team’s operational maturity, your existing infrastructure (on-premises vs. cloud), and whether managed Kubernetes offerings from your cloud provider (EKS, GKE, AKS) reduce the operational overhead.

3. Structured Logging Libraries

Log files are among the most valuable diagnostic assets an engineering team produces — and among the most frequently misused. Traditional log output is free-form text, which is easy for humans to read line by line but difficult for machines to parse, aggregate, or alert on at scale.

Structured logging solves this by emitting log entries as key-value pairs or JSON objects, making every log event immediately queryable by your observability platform. A concrete example is Logrus, a popular structured logging library for Go that is API-compatible with the standard log package. Teams can add fields such as request ID, user ID, service name, and severity level to every log entry without changing the consuming code.

Practical benefits of structured logging at the enterprise scale include:

• Faster root-cause analysis because log data can be filtered and grouped without grep pipelines.
• Integration with log aggregation platforms (Elasticsearch/OpenSearch, Splunk, Datadog) that index structured fields natively.
• Consistent log schemas across services, which is critical when dozens of teams contribute to a shared observability stack.
• Support for correlation IDs that trace a single request across multiple microservices.

Most modern languages have mature structured logging libraries. Investing time in a shared logging standard early pays dividends when debugging production incidents under pressure.

4. Advanced Monitoring and Observability Systems

Monitoring tells you that something is wrong. Observability — the combination of metrics, logs, and distributed traces — tells you why. As DevOps enterprises scale, reactive alerting on a handful of server metrics is no longer sufficient.

A mature monitoring stack typically includes:

• Metrics collection and time-series storage — tools like Prometheus scrape application and infrastructure metrics at high resolution and store them for alerting and dashboards.
• Visualization — Grafana or equivalent dashboards surface real-time performance data to on-call engineers and leadership alike.
• Distributed tracing — OpenTelemetry-instrumented services emit trace spans that reveal latency bottlenecks across microservice call chains.
• Alerting and incident management — alert rules fire when SLO thresholds are breached, routing notifications to PagerDuty, Opsgenie, or Slack.
• SaaS alternatives — platforms like Datadog, New Relic, and Dynatrace bundle all of the above with minimal infrastructure overhead, which is valuable for teams without dedicated platform engineering resources.

Monitoring containerized workloads requires tooling that understands ephemeral, dynamically scheduled pods rather than static host addresses. Ensure any solution you adopt has first-class Kubernetes support.

5. CI/CD Servers and Pipelines

Continuous integration and continuous delivery (CI/CD) is the automation backbone of a DevOps practice. CI/CD pipelines encode the steps required to take code from a developer’s commit to a production deployment as a repeatable, auditable process.

A well-designed enterprise CI/CD pipeline typically handles:

• Build automation — compiling code, resolving dependencies, and producing versioned artifacts on every commit.
• Automated testing — unit, integration, contract, and end-to-end tests run in parallel to catch regressions before they reach production.
• Security scanning — static analysis (SAST), dependency vulnerability scanning, and container image scanning integrated directly into the pipeline gate.
• Deployment orchestration — progressive delivery strategies such as canary releases, blue/green deployments, and feature flags reduce the risk of each release.
• Distributed execution — agents or runners distributed across multiple nodes reduce pipeline queue times as team size grows.

Popular CI/CD platforms include Jenkins, GitLab CI/CD, GitHub Actions, CircleCI, and Tekton (for Kubernetes-native pipelines). The right choice depends on your existing SCM platform, cloud provider relationships, and whether your team prefers self-hosted control or managed convenience.

Putting It All Together

These five technology categories are not independent silos — they reinforce each other. SCM events trigger CI/CD pipelines. Pipelines build and push container images. Running containers emit structured logs and metrics. Monitoring systems alert on those signals and trace failures back to specific commits in the SCM history. Investing in all five creates a closed feedback loop that shortens the time between writing code and learning from it in production.

For enterprises earlier in the DevOps journey, the highest-leverage starting point is usually source code management and CI/CD — both deliver immediate, visible improvements to delivery speed and code quality before you invest in the more advanced observability and containerization layers.

The Core Problem: A Failure to Communicate

If DevSecOps is not gaining traction in your organization, the root cause is usually not a tooling gap—it is a communication gap. Development, security, and operations teams have historically worked in silos, each convinced the others do not understand their constraints or priorities. The result is friction, finger-pointing, and security treated as a late-stage gate rather than a shared responsibility.

DevSecOps addresses this by integrating security into every phase of the software development life cycle (SDLC). But integration requires collaboration, and collaboration requires a cultural shift. That shift does not happen by mandate—it happens through enablement, training, and structural changes to how teams work together.

Why DevSecOps Still Struggles in Many Organizations

DevSecOps has been an active area of practice for several years, yet many organizations still treat security as an afterthought bolted onto the end of a CI/CD pipeline. Several structural factors contribute to this:

• Team isolation: Development, security, and operations teams often have separate reporting lines, separate toolchains, and separate incentives.
• Security staffing ratios: Security teams are significantly outnumbered. Industry estimates suggest that for every 100 developers in a typical DevOps environment, there may be roughly 10 operations staff and as few as one security professional—making it structurally impossible for security to review everything.
• Late-stage security reviews: Finding a vulnerability during a pre-release security scan is far more expensive to remediate than catching it at the code-writing stage. Rework costs increase significantly the later a defect is discovered in the SDLC.
• Language barriers: Security professionals speak in terms of threat models and CVEs; developers speak in terms of features and deadlines. Without a shared vocabulary, neither side communicates effectively.

Two Tiers of Security Training to Accelerate DevSecOps Adoption

One of the most practical approaches to closing the communication and capability gap is a two-tier security training model: baseline secure coding training for all developers, and a deeper security champions program for a motivated subset of those developers.

Tier 1: Baseline Secure Coding Training for All Developers

The goal of baseline training is not to turn every developer into a security engineer. It is to give developers enough security awareness that they make fewer critical mistakes during day-to-day coding. Any developer who cares about code quality should find this framing compelling: security is a dimension of quality, not a separate discipline.

Good starting points for curriculum include publicly available, well-maintained references:

• OWASP Top 10: The Open Web Application Security Project’s ranked list of the most critical web application security risks. Updated periodically, it covers issues such as injection, broken authentication, and security misconfiguration.
• CWE Top 25: MITRE’s list of the most dangerous software weaknesses, useful for understanding vulnerability classes at the code level.

Core topics that baseline training should cover include:

• Injection attacks (SQL, command, LDAP)
• Authentication, authorization, and access control failures
• Sensitive data exposure and proper handling
• XML External Entity (XXE) processing risks
• Security misconfiguration
• Cross-Site Scripting (XSS)
• Insecure deserialization
• Insufficient logging and monitoring
• Use of components with known vulnerabilities

Training formats that tend to work well in engineering cultures include hands-on labs, capture-the-flag (CTF) exercises, and integrating security scenarios into existing code review workflows—rather than passive slide-deck presentations.

Tier 2: Security Champions Program

A security champions program identifies a small number of developers within each team who receive deeper security training and serve as the primary security liaison between their development team and the central security team. The concept is grounded in a straightforward insight: a peer is more effective at communicating security concepts to developers than an outsider.

Key design principles for an effective security champions program:

• Volunteer-based: Security champions should self-select based on genuine interest in security. Conscripting reluctant participants undermines the program’s credibility and the champion’s effectiveness.
• Career development framing: Position the role as a professional growth opportunity. Security knowledge is increasingly valued, and developers who build it become more marketable.
• Structured training and support: Champions need ongoing education—not just a one-time course. This might include access to advanced training, regular syncs with the security team, and participation in threat modeling sessions.
• Defined responsibilities: Champions should have a clear remit, such as conducting first-pass security reviews of pull requests, running secure coding workshops for their team, or triaging security scanner output.
• Recognition: Acknowledge champions’ contributions publicly within the organization to reinforce the value of the role.

Given typical staffing ratios, even one engaged security champion per development team can meaningfully extend the reach of a small central security team.

Shifting Left: Building Security Into the SDLC from the Start

Training initiatives are most effective when paired with process changes that embed security earlier in the development workflow—a practice commonly called “shifting left.”

Practical ways to shift security left in a CI/CD pipeline include:

• Static Application Security Testing (SAST): Integrate SAST tools into the IDE or as a pre-commit hook so developers receive feedback on vulnerable code patterns before a pull request is even opened.
• Software Composition Analysis (SCA): Automatically scan dependencies for known vulnerabilities as part of the build process.
• Threat modeling during design: Involve security champions or the security team in architecture and design reviews, not just post-implementation audits.
• Security in definition of done: Add security acceptance criteria to user stories and sprint definitions so security is a standard part of the completion checklist.
• Automated security gates in CI/CD: Configure pipelines to fail builds that introduce high-severity vulnerabilities, creating an immediate feedback loop.

When developers catch and fix security issues at the point of introduction, the number of vulnerabilities that escalate to the security team drops substantially. This reduces the single largest source of conflict between development and security teams: the back-and-forth cycle of security rejecting a release and sending fixes back to developers under deadline pressure.

The Cultural Shift Is the Hard Part

Tools and training are enablers, but the underlying challenge is cultural. DevSecOps works when development, security, and operations are not treated as separate organizational units with competing priorities, but as different specializations within a unified team sharing a single goal: delivering secure, high-quality software efficiently.

Practically, this means:

• Security metrics should be visible to the whole team, not just the security function.
• Post-incident reviews should involve developers and operations, not just security responders.
• Security should be present in sprint planning and backlog grooming conversations, not just at release gates.
• Leadership should recognize and reward security contributions from developers, not only feature delivery.

The principle that guides this entire approach is worth stating plainly: make the secure way the easier way. When developers have the knowledge, the tools, the peer support, and the process scaffolding to write secure code without significant extra effort, security becomes a natural output of good engineering rather than an obstacle to it.

Summary

Championing security in DevOps is less about buying the right scanner and more about changing how teams think and communicate. A two-tier training approach—baseline secure coding education for all developers combined with a well-structured security champions program—gives organizations a practical path toward DevSecOps maturity. Pair that with left-shifted security tooling in the CI/CD pipeline and a deliberate cultural commitment to shared ownership of security, and the result is faster delivery of software that is also genuinely more secure.

CCV Partners with AWS Academy to Expand Cloud Education

The Community College of Vermont (CCV) has joined Amazon Web Services (AWS) Academy, a program that equips higher education institutions with cloud computing curriculum and resources. The partnership allows CCV to offer students a structured path toward industry-recognized cloud certifications and in-demand IT careers.

What AWS Academy Provides

AWS Academy supplies member institutions with curriculum developed and maintained by AWS subject matter experts. This ensures the course content reflects current AWS services, tools, and best practices. Courses are delivered by AWS Academy-accredited educators who receive specific training to help students become proficient with AWS technology.

The program is designed to bridge the gap between classroom instruction and real-world cloud skills, giving students a competitive edge when entering the job market or pursuing AWS certifications.

The Course: Fundamentals of Cloud Computing

CCV incorporated AWS Academy content into a new course titled “Special Topics: Fundamentals of Cloud Computing.” The course was offered online in fall 2020, with classes beginning September 15, 2020.

Key details about the course include:

• Target audience: Beginners exploring IT careers, remote work opportunities, or anyone looking to develop foundational cloud skills applicable across industries.
• Certification goal: Prepares students to sit the AWS Certified Cloud Practitioner exam, one of AWS’s entry-level certifications.
• Degree integration: Credits can be applied toward CCV’s IT or STEM Studies degree programs.
• Delivery: Fully online, consistent with CCV’s broader distance learning infrastructure.

Why This Matters for Students and Employers

Cloud computing skills consistently rank among the most sought-after competencies in the IT job market. The AWS Certified Cloud Practitioner credential is a vendor-recognized certification that validates foundational knowledge of the AWS platform, cloud concepts, security, pricing, and support models. Earning it can open doors for career changers and IT professionals looking to formalize their cloud knowledge.

Nicole Stetson, Associate Academic Dean for CCV, commented on the partnership:

“CCV is pleased to offer new coursework and training in cloud computing. The fall course is designed for beginners exploring careers in IT and remote work opportunities as well as those looking to build cloud knowledge and skills that can be applied to any business. Participants can earn an industry-recognized AWS Certification and use the course in CCV’s IT or STEM Studies degree programs.”

Nicole Stetson, Associate Academic Dean, Community College of Vermont

About the Community College of Vermont

CCV is Vermont’s second-largest college, serving more than 10,000 students annually. The institution operates 12 locations across the state and offers extensive online learning options, making higher education accessible to students throughout Vermont without requiring long commutes. CCV provides degree and certificate programs, workforce training, continuing education, and academic and veterans support services.

Getting Started with AWS Certifications

For DevOps practitioners and IT professionals interested in formalizing their AWS knowledge, the AWS Certified Cloud Practitioner exam is a broadly recognized starting point. It covers:

• Core AWS services (compute, storage, networking, databases)
• Cloud concepts and the AWS shared responsibility model
• AWS billing, pricing models, and support plans
• Basic cloud security and compliance concepts

From there, practitioners can pursue associate-level certifications such as AWS Solutions Architect, Developer, or SysOps Administrator, depending on their career focus.

Institutions interested in joining AWS Academy can find more information on the AWS Academy program page.

Five Years in the Making: Ansible for DevOps, 2nd Edition

After five years of continuous development, 834 commits, and 24 major revisions, Jeff Geerling published the 2nd edition of Ansible for DevOps. The book has sold over 60,000 copies and served as the foundation for a free Ansible 101 video series on YouTube. The 2nd edition marks the start of the 2.x revision series.

The book covers a broad range of infrastructure automation topics:

• Managing Linux servers with Ansible basics
• Continuous integration pipelines
• Application deployments
• Container image management
• Kubernetes cluster management with Ansible

The Problem with Traditional Edition-Based Publishing

Geerling’s original commitment was simple: ebook updates would be free, forever. He has published at least one new revision per quarter since the first release (version 0.42 on LeanPub in 2014). However, selling through Amazon introduced friction: Amazon follows the traditional publishing convention of “editions,” where any substantive update beyond minor corrections requires a new edition — complete with a new ISBN and a new product listing.

This model made sense in the era of print, but it creates a poor experience for readers of digital technical books. Anyone who has owned outdated print books on Python 2, PHP 5, or legacy Linux distributions understands the problem. Technology moves faster than print publishing cycles, and readers who buy digital copies deserve to benefit from an author’s ongoing improvements.

What an Evergreen Technical Book Looks Like in Practice

Keeping a technical book genuinely current requires more than occasional edits. Geerling’s approach to maintaining Ansible for DevOps as an evergreen resource included:

• Quarterly revisions to reflect new Ansible releases, deprecation cycles, and evolving best practices.
• Chapter rewrites when underlying architecture shifted significantly enough to make older guidance misleading.
• A companion GitHub repository containing all code examples from the book, with CI tests that run on a weekly schedule to catch bit rot — examples that silently break as dependencies, APIs, or Ansible modules change.
• An open issue tracker where readers can report broken or confusing examples, and submit patches directly.

The CI testing approach is particularly notable for a book project. Rather than relying solely on manual review, automated weekly test runs surface problems proactively, before readers encounter them.

The Financial Case for Self-Publishing Technical Books

Geerling is candid about the economics. Over five years, Ansible for DevOps earned him nearly $200,000 in royalties — after platform fees of 30–70% from Amazon, Apple Books, and LeanPub (LeanPub being the most author-generous of the three).

For context, a comparable deal with a traditional publisher might have yielded:

• A few thousand dollars as an advance
• Roughly a 5% royalty rate on net sales
• No royalties until the advance was earned out
• An estimated total return of under $50,000 for the same sales volume

Self-publishing platforms like LeanPub typically return 70–80% of the sale price to the author. Amazon’s cut varies by price tier and format. The gap in author earnings between self-publishing and traditional publishing is substantial, and for a successful title it can represent a difference of four times or more in total income.

“Self-publish, don’t write for a publisher.” — Jeff Geerling

Honest Advice: Don’t Quit Your Day Job

Despite Ansible for DevOps being a genuinely successful technical book — one of the more successful self-published DevOps titles available — Geerling is direct: $200,000 over five years averages $40,000 per year. That is meaningful supplemental income, but it does not replace the salary a skilled DevOps engineer or software architect can command.

His conclusion for anyone considering writing a technical book:

• If you have deep, practical expertise in a cloud or infrastructure topic, your salary as a practitioner will likely exceed your maximum book revenue by a factor of five or more.
• Write the book as a practitioner, not as a full-time author. The best technical books come from people solving real problems in production environments, not from authors who write to fill a publisher’s catalog.
• “Fluff” tech books — written by light users of a tool or by serial authors without hands-on depth — are immediately apparent to experienced readers and rarely offer the small, high-value tips that justify the purchase price.

LeanPub and the Lean Publishing Model

One of the practical enablers of Geerling’s approach is LeanPub, a publishing platform designed for iterative, in-progress book writing. Key properties that make it well-suited for technical authors:

• You can publish and sell a partial, in-progress manuscript immediately.
• Readers who buy early can engage with the content and provide feedback while the book is still being written.
• Updates to buyers are automatic and free — no new purchase required.
• Author royalty rates are significantly higher than traditional publishers or even most major retail platforms.

This model validates demand early, reduces the risk of spending months writing a book nobody wants, and aligns author incentives with reader experience over the long term.

What This Means for DevOps Engineers Considering Writing

If you are a DevOps or platform engineering practitioner with genuine depth on a specific tool or workflow, self-publishing a technical book is a viable way to build credibility, contribute to the community, and earn supplemental income. The key takeaways from Geerling’s experience:

• Start lean. Publish early on a platform like LeanPub and iterate publicly.
• Automate quality assurance. Treat code examples like production code — use CI to test them on a regular schedule.
• Commit to maintenance. A technical book that goes stale within a year damages your reputation more than not writing one at all.
• Self-publish first. Traditional publishing deals rarely make financial sense for technical authors with the motivation to self-publish.
• Keep your day job. Even a successful technical book is unlikely to match your engineering salary.

About the Books

Ansible for DevOps, 2nd Edition is available on Amazon, LeanPub, and Apple Books. Jeff Geerling is also writing Ansible for Kubernetes, a deeper dive into Kubernetes and container automation with Ansible, available in progress on LeanPub with the same free-updates-forever commitment.

For more from Jeff Geerling, see jeffgeerling.com.

The Problem: Kubernetes Was Built for Production, Not for ML Experimentation

If your organization is running machine learning workloads, you have almost certainly run into a familiar tension: data science teams need enormous compute resources, and your Kubernetes cluster is right there. The temptation to hand them cluster access is real. The consequences, however, are predictable.

Kubernetes was engineered for running stable, production-grade, replicated services. ML and deep learning (DL) workloads are almost the exact opposite: they are experimental, ephemeral, highly variable in resource demand, and they change constantly. Pretending otherwise creates operational pain for everyone involved.

What Data Scientists Actually Need

Data scientists are researchers first. Their job is to develop models, tune hyperparameters, and run experiments — often hundreds or thousands of them concurrently. To do that effectively, they need:

• Remote compute with GPU access. Local laptops typically lack the memory, storage, and GPU capacity required for modern deep learning workloads.
• Fast environment iteration. Experiment code changes frequently. Keeping Dockerfiles, requirements.txt files, and Conda YAML manifests up to date manually is a distraction from the actual science.
• Domain-specific monitoring. They care about model accuracy, training loss curves, and experiment throughput — not CPU utilization graphs.
• Queue-based job scheduling. Running many concurrent experiments that are short-lived relative to server uptime is an HPC-style challenge, not a standard Kubernetes workload pattern.

Why Giving Data Scientists Raw Kubernetes Access Is a Bad Idea

On a well-managed Kubernetes cluster, a set of operational rules exist for good reason. Granting unrestricted access to ML teams tends to violate most of them:

• Placing all pods in a single namespace.
• Manually managing large numbers of heterogeneous pod types and repository workloads.
• Attempting to control Kubernetes scheduler ordering — which is not reliably possible.
• Leaving thousands of stale, single-use container images on the cluster.
• Triggering uncontrolled node autoscaling in response to a flood of pending experimental jobs.

Unlike traditional software deployment — where a tested, stable artifact is replicated across nodes — ML experimentation means continuously deploying unstable, in-progress code at scale. A cluster of tens of thousands of one-off container images is not a hypothetical; it is a common outcome.

Breaking Down the Real Requirements

The needs of DevOps teams and data science teams differ significantly across every dimension of infrastructure management. Understanding the gap is the first step toward bridging it.

Resource Access

For a DevOps engineer, resource access means namespaces, RBAC policies, network policies, and reliability. For a data scientist, it means: Is this a CPU machine or a GPU machine, and how many GPUs does it have? A simple tiered model — CPU, 1×GPU, 2×GPU, 8×GPU — typically covers the majority of AI team requirements. The nuance that matters to operations is largely irrelevant to the researchers.

Environment Packaging

From a DevOps perspective, environment packaging means containerizing a stable codebase. For ML teams, the development environment changes constantly. Manually maintaining Dockerfiles and dependency manifests is time-consuming and error-prone — stale configurations accumulate quickly because they are decoupled from the actual coding environment. What is needed is automatic extraction of the environment state, with seamless replication from local development machines to remote execution pods.

Monitoring

DevOps monitoring focuses on infrastructure health: CPU usage, memory pressure, disk I/O, pod restarts. Data science monitoring focuses on experiment health: training loss, validation accuracy, throughput, and custom business-relevant metrics. No universal standard exists for ML experiment monitoring, and the ability to define and track custom metrics is often what separates a productive ML team from one flying blind.

Job Scheduling

Kubernetes job scheduling is oriented around resource allocation for long-running or indefinite workloads. ML job scheduling is closer to an HPC queuing problem: there are always more experiments queued than available resources, jobs are relatively short-lived, and the scheduling policy — which job runs first, on which resources, with what priority — directly affects research velocity. Kubernetes was not designed to optimize for this pattern.

MLOps: A Purpose-Built Layer for AI Workloads

MLOps tooling has emerged specifically to address this gap. Rather than replacing Kubernetes, a well-designed MLOps platform sits on top of it — presenting a resource interface tailored to data science teams while letting Kubernetes continue to do what it does well: managing the underlying hardware cluster.

From a Kubernetes perspective, the MLOps platform is just another application: a replicated service with a known resource footprint, deployed and managed like anything else on the cluster. From the data science team’s perspective, it exposes their allocated compute nodes as a logical cluster they control directly.

What an MLOps Platform Provides

• Dedicated job queues with priority ordering, experiment tracking, and isolation from Kubernetes scheduler internals.
• Automatic environment packaging that extracts and replicates the current development environment into a container without requiring the data scientist to manually maintain Dockerfiles.
• Experiment-centric monitoring with customizable metrics dashboards suited to model development workflows.
• Lifecycle management for experiments, including the ability to terminate runaway jobs without DevOps intervention.
• Self-service provisioning so that the AI team can launch, monitor, and clean up their own workloads within the resource budget already allocated by the DevOps team.

The Resulting Division of Responsibility

With an MLOps layer in place, the operational model becomes significantly cleaner:

• DevOps team: Manages Kubernetes, allocates hardware resources to the MLOps platform, and updates resource allocations when needed. They are not in the critical path for every experiment run.
• Data science team: Self-services through the MLOps platform. They launch experiments, track results, manage their queues, and iterate on models without needing to understand Kubernetes internals or file infrastructure tickets.

The MLOps solution acts as the glue: it picks jobs from the ML team’s queues, provisions the appropriate environment (inside a container or as a sidecar), submits the workload to Kubernetes with proper resource requests, monitors execution, and handles cleanup — all without manual DevOps involvement.

Key Takeaways

• Kubernetes is the right tool for managing production infrastructure and hardware clusters. It is not the right tool for managing ML experiment workflows directly.
• Data science teams have fundamentally different requirements for resource access, environment management, monitoring, and scheduling than traditional software engineering teams.
• MLOps platforms address these requirements by providing a purpose-built interface that runs on top of Kubernetes without disrupting its operational model.
• A clean MLOps integration reduces the operational burden on DevOps teams, eliminates cluster sprawl, and gives data scientists the autonomy they need to iterate quickly.

The payoff is straightforward: fewer 2 a.m. provisioning calls, fewer stale container images clogging your registry, and a data science team that can actually focus on building models rather than filing infrastructure tickets.

Why Choose Open Source Development Tools?

The tools that make up your development stack will vary depending on your programming language, deployment targets, and team workflows. Common categories include source code management, integrated development environments (IDEs), text and source editors, continuous integration (CI) servers, and configuration management systems.

Development teams can always license tools from proprietary vendors, but open source alternatives offer several meaningful advantages:

• Cost: Many open source tools are free to use, with optional paid tiers for enterprise support, allowing teams to evaluate tools before committing budget.
• Extensibility: Widely adopted open source tools typically spawn rich ecosystems of plugins, add-ons, and community-maintained extensions.
• Broad compatibility: Many open source tools are platform-agnostic or explicitly designed to integrate with a large number of platforms and languages.
• Longevity: Community-maintained projects often continue to receive support long after an original sponsoring organization steps back, reducing vendor lock-in risk.

10 Top Open Source Development Tools

1. Git

Git is a distributed version-control system that is the de facto standard for source code management in modern software development. It is commonly paired with web-based hosting platforms such as GitHub and GitLab, which add pull requests, code review workflows, and CI/CD integrations on top of Git’s core branching and merging capabilities. Teams working primarily on Windows should be aware that Git’s Linux roots mean some edge-case behaviors and tooling integrations can feel less polished on Windows than on Linux or macOS, though this gap has narrowed considerably in recent years.

2. Apache Subversion (SVN)

Apache Subversion, widely known as SVN, is a centralized version-control system. Unlike Git, which defaults to local repositories, SVN stores all code history on a central server. This centralized model can simplify access control and auditing in regulated environments. Teams that need to interoperate with both SVN and Git repositories can use the git-svn bridge, which allows standard Git commands to read from and write to Subversion repositories.

3. Eclipse IDE

Eclipse is a mature, open source integrated development environment written primarily in Java. It is most widely used for Java development but supports a broad range of programming languages through its plugin marketplace. That extensibility is one of Eclipse’s greatest strengths, though it also introduces a potential complication: some plugins declare dependencies on other plugins, which can make it difficult to add or remove individual extensions without inadvertently breaking existing functionality.

4. Apache NetBeans

NetBeans is another Java-based open source IDE that supports development across many programming languages. Where Eclipse leans on its plugin ecosystem to unlock functionality, NetBeans takes a more batteries-included approach, providing a wider range of features out of the box. This can mean less initial configuration work, though it may also mean a heavier default installation footprint. NetBeans became an Apache project in 2016, which strengthened its governance and long-term support story.

5. GNU Emacs

Emacs is a highly extensible open source text editor with roots in the GNU project dating to the mid-1980s. Beyond basic editing, Emacs can be configured into a full IDE with support for language servers, debuggers, version control, and email. It supports complex automation through its macro system and the built-in Emacs Lisp scripting language. The trade-off is a notoriously steep initial learning curve and a time investment in configuration before the environment feels productive.

6. Vim

Vim is another venerable open source text editor with a loyal user base. It starts quickly and runs efficiently even on resource-constrained systems. Like Emacs, Vim can be extended into a near-complete development environment through plugins. The long-running debate over which editor has the steeper learning curve — Vim or Emacs — is unlikely to be settled here, but both remain excellent, proven choices for developers who prefer staying close to the terminal. Neovim, a modern fork of Vim, has gained significant traction for its improved plugin architecture and built-in support for the Language Server Protocol (LSP).

7. Atom

Atom was developed by GitHub and marketed as a “hackable” text editor aimed at modern development workflows. It offered deep GitHub integration, built-in collaborative editing via Teletype, and a large package ecosystem. However, GitHub officially sunset Atom in December 2022, citing the growth of Visual Studio Code as a primary reason. Teams currently using Atom should plan migration to an actively maintained alternative such as VS Code, Neovim, or Helix.

8. Jenkins

Jenkins is one of the most widely deployed open source CI/CD servers in the industry. Its extensive plugin ecosystem — numbering in the hundreds of plugins — enables integration with nearly any version control system, build tool, testing framework, or deployment target. Jenkins can run on any modern operating system and also inside Docker containers, making it flexible for both on-premises and cloud-based CI pipelines. Common operational considerations include plugin compatibility management and the maintenance overhead of keeping a self-hosted Jenkins instance patched and performant.

9. Chef

Chef is an open source configuration management tool that uses a Ruby-based domain-specific language to define infrastructure as code. Administrators write “cookbooks” and “recipes” that describe the desired state of a system; Chef then converges the actual system state to match those specifications. Chef provides cross-platform support for Linux, macOS, and Windows. The Ruby DSL offers significant expressive power but adds a learning curve for teams not already familiar with Ruby.

10. Ansible

Ansible is an agentless open source automation and configuration management tool that uses YAML-based playbooks to define infrastructure and application configuration. Because it connects to managed nodes over SSH rather than requiring a dedicated agent, initial setup is generally simpler than Chef. This agentless design also reduces the attack surface on managed hosts. The trade-off is that Ansible’s declarative model and module library, while broad, may be less suited to highly complex, custom configuration scenarios where Chef’s full Ruby expressiveness provides an advantage. Windows support exists but is generally considered less mature than Linux support.

Choosing the Right Tool for Your Stack

No single tool excels in every context. The right open source development stack depends on your team’s programming languages, operating system targets, existing infrastructure, and operational maturity. Start by identifying the category of tooling you need — version control, IDE, CI server, or configuration management — then evaluate the options in that category against your specific requirements. The open source community around each of these tools means documentation, tutorials, and community support are readily available regardless of which tools you choose.

Original article reference: searchapparchitecture.techtarget.com

Nutanix Clusters Land on Amazon EC2 Bare-Metal

Nutanix announced in August 2020 that Nutanix Clusters are now available on bare-metal instances of Amazon Elastic Compute Cloud (Amazon EC2), marking the company’s first major step into public-cloud-hosted hyperconverged infrastructure (HCI). The move allows enterprises already running Nutanix on-premises to extend those environments into AWS without rearchitecting their workloads.

What Is Nutanix Clusters on AWS?

Nutanix Clusters brings the company’s HCI software stack — which converges compute, storage, and networking management — to AWS EC2 bare-metal instances. Because these instances expose the underlying hardware directly rather than through a hypervisor layer, Nutanix can run its own hypervisor and storage fabric on top, replicating the on-premises experience inside AWS data centers.

Manoj Agarwal, then Senior Vice President of Engineering at Nutanix, described the value proposition clearly: AWS does not natively offer HCI capabilities based on the Nutanix virtual machine stack, so Nutanix Clusters fills a gap for organizations that want HCI-style infrastructure management in the public cloud.

Key Capabilities and Benefits

No Workload Refactoring Required

One of the primary selling points is that IT organizations can lift and shift existing on-premises Nutanix clusters to AWS without refactoring applications. Because the underlying platform is identical, VMs and workloads behave the same way in the cloud as they do on-premises.

Unified Management via Nutanix Prism

IT teams can provision and manage Nutanix Clusters on AWS using Nutanix Prism — the same management console used for on-premises deployments. This single management plane eliminates the need to learn cloud-native tooling or maintain separate operational workflows for cloud and on-premises environments.

Access to the Full Nutanix Add-On Ecosystem

Running Nutanix on AWS also unlocks the broader suite of Nutanix add-on services, including:

• Nutanix Files — unstructured data management and file services
• Nutanix Calm — application orchestration and automation
• Nutanix Era — database lifecycle management and provisioning

These services function consistently whether the underlying cluster is in an on-premises data center or on AWS, reinforcing the goal of a single operational model across environments.

The Hybrid Cloud Context: Why This Matters

The announcement arrived during a period of significant disruption. The economic pressures of the COVID-19 pandemic accelerated hybrid cloud adoption as IT teams looked for ways to reduce total cost of ownership and increase operational flexibility. Many organizations had historically managed cloud and on-premises environments as completely separate silos — each with its own tooling, processes, and operational teams.

Nutanix Clusters on AWS directly addresses this fragmentation. Rather than forcing IT teams to choose between a dedicated on-premises HCI platform and native cloud services, it allows them to span both using a common infrastructure and management layer.

Avoiding Cloud Lock-In

Agarwal also positioned Nutanix Clusters as a hedge against cloud vendor lock-in. By deploying a consistent HCI platform across on-premises and cloud environments — and potentially across multiple clouds — IT organizations can move workloads more freely without being tightly coupled to any single cloud provider’s proprietary services.

Notably, at the time of the announcement, Nutanix declined to confirm whether Clusters support would be extended to other public cloud platforms beyond AWS. The multi-cloud angle remained aspirational rather than immediately actionable for non-AWS environments.

How Nutanix Competes in the Hybrid Cloud Management Race

Nutanix is not the only vendor pursuing the role of a unified hybrid cloud management plane. Cloud providers themselves have moved in the opposite direction — making managed, on-premises versions of their cloud platforms available (for example, AWS Outposts, Google Anthos, and Azure Arc). These offerings let cloud vendors manage on-premises infrastructure on behalf of customers.

Nutanix’s approach differs in a meaningful way: IT teams retain direct control of their infrastructure and operational model. Rather than delegating management to a cloud provider, organizations use Nutanix tooling to automate hybrid cloud operations themselves. This resonates particularly with enterprises that have regulatory, data sovereignty, or operational reasons to maintain direct infrastructure control.

Summary

Nutanix Clusters on AWS represents a significant milestone for enterprise HCI in the public cloud. By running on EC2 bare-metal instances and integrating with the existing Nutanix Prism management layer, it gives IT teams a credible, low-disruption path to hybrid cloud — extending familiar on-premises infrastructure into AWS without sacrificing consistency, tooling, or control. Whether this approach becomes a dominant hybrid cloud model will depend on how quickly organizations can consolidate fragmented cloud and on-premises operations under a single management framework.

The following analysis draws on themes from the State of Cloud Native Security Report 2020 published by Palo Alto Networks—described at the time of writing as one of the largest globally distributed market research datasets on cloud-native security. The data points below are sourced from that report.

Cloud-Native Architectures and What They Mean for DevOps Security

Cloud Workloads Are Growing—Rapidly

Survey respondents estimated that, on average, 46% of all workloads were already running in the cloud at the time of the survey. Ninety-five percent of respondents expected that share to grow over the following 24 months. For DevOps teams, this trajectory means more deployments, more managed services, and more surface area that security controls must cover.

Cloud Infrastructure Is Constantly Changing

Eighty percent of survey respondents said their company’s cloud infrastructure is continuously evolving—driven by experimentation with new services, evaluation of emerging cloud offerings, and iterative refinement of best practices. This constant change creates a significant challenge for security teams: tooling and policies calibrated for today’s architecture may be outdated by next quarter.

That concern is widely felt: 75% of respondents worried that cloud threats already outpace the organization’s security capabilities. Continuous change is not inherently bad for DevOps, but it demands that security practices keep the same cadence as infrastructure updates—not lag behind them.

Multi-Cloud Is the Norm, Not the Exception

Nearly every organization surveyed—90%—confirmed they use more than one cloud provider. Workloads are spread across a mix of compute architectures:

• Virtual Machines (VMs): ~30% of workloads on average
• Containers: ~24%
• Platform-as-a-Service (PaaS): ~22%
• Container-as-a-Service (CaaS): ~21%

This diversity of platforms and compute models means security teams need both broader and deeper visibility across the entire infrastructure estate. The survey found that 57% of security teams were already using more than five distinct security tools, with some organizations running eleven or more. More tools often means more dashboards, more policy engines, and more manual effort to correlate findings—a problem of its own.

The Threat Landscape: No Single Dominant Risk

When asked to name their single greatest cloud security threat, respondents spread their answers across ten different categories—none commanding a clear majority. This distribution itself is important: there is no obvious chokepoint to secure, which makes prioritization harder and collaboration between development and security teams more essential.

The near-equal distribution across these categories explains why DevOps and security teams sometimes disagree on priorities. Without a single dominant threat to rally around, teams must negotiate which risks deserve the most immediate attention—a conversation that works best when security is embedded in development workflows from the start rather than bolted on at the end.

Embedding Security Across the Full Application Life Cycle

The report ranked organizations by their level of security preparedness and compared the habits of the highest-ranked cohort against lower-ranked peers. The results support a clear pattern: organizations with the strongest security posture treat security as a continuous, integrated practice rather than a gate at the end of the pipeline.

• 45% of highly prepared organizations embed security directly into their DevOps processes.
• 41% integrate security controls at four or more stages of the development life cycle.
• Leading organizations automate key security practices—including configuration monitoring and vulnerability scanning—and implement guardrails or quality gates that block insecure code from advancing through the pipeline.

The practical implication for DevOps teams is straightforward: shift security left. Identify vulnerabilities during the design and development stages, not after code is deployed. This means selecting CI/CD tooling and cloud security platforms that can run policy checks, scan container images, and validate infrastructure-as-code templates automatically at each stage.

DevOps teams should also work with security leadership to evaluate consolidated platforms capable of spanning multiple cloud providers and compute types. Replacing a fragmented collection of point tools with a platform that provides unified visibility and integrated policy enforcement reduces complexity and makes continuous security monitoring operationally sustainable.

Practical Next Steps for DevOps Teams

The data from the 2020 report points to a clear direction of travel for teams navigating cloud-native security:

1. Accept that multi-cloud and hybrid compute is permanent. Design security controls that work across VM, container, PaaS, and CaaS workloads from the outset rather than retrofitting later.
2. Shift security left. Integrate static analysis, secrets scanning, dependency checks, and policy validation as early as the developer’s local environment and certainly within every CI pipeline run.
3. Automate configuration monitoring. Infrastructure misconfigurations and over-permissioned access together account for a significant share of prioritized threats. Automated drift detection and least-privilege enforcement reduce this risk without adding manual toil.
4. Reduce tool sprawl. If your organization is running more than five security tools across the cloud estate, evaluate whether a consolidated platform can reduce the operational overhead of correlating findings across dashboards.
5. Increase collaboration between development and security teams. The breadth of the threat landscape means no single team can own cloud security alone. Shared threat models, joint incident reviews, and co-authored security requirements help both sides align on priorities.

Summary

Cloud-native adoption is accelerating, and that acceleration is not slowing down. The organizations that navigate this transition most effectively are the ones treating security as a continuous, automated, life-cycle-wide responsibility rather than a compliance checkpoint. For DevOps teams, that means advocating for integrated security tooling, embedding controls early in the pipeline, and building the kind of collaborative relationship with security teams that enables fast delivery without unacceptable risk.

Why Government and DevOps Are a Natural Fit

At first glance, bureaucratic government agencies and the agile world of DevOps seem like an unlikely pairing. But according to Van Kalken, the missions are closely aligned: government organisations exist to deliver services to the community, and DevOps is fundamentally about delivering reliable, iterative services to a community of users.

Van Kalken’s observation—drawn from working with agencies at local, state, and federal levels in Australia—is that government entities are, in some respects, further along the DevOps maturity curve than their private-sector counterparts. The reasons are structural, cultural, and regulatory, and each offers a lesson for enterprises that are still siloed or struggling to embed security into their delivery pipelines.

Shift-Left Security in Practice

One of the clearest indicators of DevOps maturity is how early security is introduced in the software delivery lifecycle. In government agencies that Van Kalken works with, security is no longer an afterthought bolted on at the end of a project—it is embedded from day one. This “shift-left” approach means vulnerabilities are caught earlier, when they are cheaper and faster to fix, and compliance evidence is generated continuously rather than assembled in a last-minute audit scramble.

The COVID-19 pandemic provided a real-world stress test. As remote-working mandates took effect, government departments had to scale their infrastructure rapidly and securely. Van Kalken cited one large department that expanded its remote workforce from 1,000 to 20,000 people in three to four days. This was possible, he said, because the department automated virtually every step of its infrastructure and security policy deployment, using DevOps practices to spin up cloud capacity with the correct security configuration already baked in. Teams also used APIs to apply security policies to SaaS applications quickly and consistently.

Cross-Functional Collaboration Without Turf Wars

Van Kalken described a government department that was new to DevOps but excelled at something many experienced DevOps organisations still struggle with: getting all the relevant stakeholders—development, operations, security, and policy—into the same room and focused on a shared outcome. There were no territorial disputes over ownership. Security requirements were treated as part of the definition of “done,” not as a separate workstream.

In contrast, private-sector organisations often remain siloed. Development teams optimise for feature velocity, security teams focus on compliance, and operations teams manage uptime—each with different KPIs and sometimes competing incentives. This fragmentation leads to the classic late-stage security review that slows delivery and introduces rework.

The shift Van Kalken describes is a change in the role of the security function itself: from gatekeeper—what he colourfully likens to a nightclub bouncer whose default answer is “no”—to a collaborative partner embedded in the delivery team.

Regulatory Alignment as an Unlikely DevOps Accelerator

Most enterprises adopt one of the common security frameworks (such as NIST CSF, ISO 27001, or SOC 2) and then leave individual project teams to interpret how to comply. That flexibility can breed inconsistency.

Australian government agencies operate under a mandatory baseline: the Australian Government Information Security Manual (ISM), published by the Australian Signals Directorate. Because every department must meet the same minimum bar, there is a built-in alignment that reduces the negotiation overhead between teams. Shared standards create a shared language, and that accelerates cross-departmental DevSecOps collaboration.

Private-sector organisations that choose a framework and then apply it inconsistently across business units could benefit from enforcing a single, organisation-wide baseline—even if it is stricter than the minimum any given project requires.

Speed as a Security Tool

Government systems hold sensitive personal data and are actively targeted by both opportunistic attackers and nation-state actors. Van Kalken notes that government agencies face the same threat landscape as banks and large retailers, with the added dimension of politically motivated attacks.

DevOps practices help agencies respond faster than traditional change-management processes allow. Concrete examples include:

• Automated policy deployment: New firewall rules or access-control policies can be pushed to distributed infrastructure in minutes rather than days, using infrastructure-as-code and CI/CD pipelines.
• Faster patch cycles: Automating the testing and deployment of commercial software updates reduces the window between a CVE disclosure and a patched production environment.
• Freed-up expertise: When routine policy changes are automated, skilled security engineers can focus on anomalies and higher-order threats rather than repetitive manual tasks.

“That element of speed is something very new to the security space,” Van Kalken said—and it is precisely where DevOps practices offer the most immediate, tangible security benefit.

Actionable Recommendations for Private-Sector Teams

Van Kalken’s advice distils down to four practical steps that any organisation can begin implementing regardless of its current DevOps maturity:

1. Bring security in from day one. Include security engineers in project kick-offs, sprint planning, and architecture reviews—not just in pre-release gates.
2. Align on a shared objective. Every team member, regardless of function, should be able to articulate the outcome the project is trying to achieve and why security is part of that outcome.
3. Swim in the same direction. Eliminate competing KPIs that reward one team for speed at the expense of another team’s compliance metrics.
4. Sustain collaboration continuously. Cross-functional alignment is not a kickoff exercise—it requires ongoing communication, shared tooling, and joint retrospectives.

“The rest will just happen if you do these things,” Van Kalken said. The implication is that most of the friction in DevSecOps adoption is organisational, not technical.

Key Takeaway

The stereotype of government as a slow-moving technology laggard does not hold universally. In Australia, at least, some agencies are demonstrating that a laser focus on service delivery, mandatory security standards, and genuine cross-functional collaboration can produce DevOps outcomes that private enterprises are still aspiring to. The lesson is not to copy government bureaucracy—it is to copy government’s clarity of mission and commitment to shared security ownership.

From IT Stack to Data Infrastructure

The concept of the “IT stack” has evolved significantly. Where once it described software running across a mix of hardware resources — tied together by analytics tools, network topology controls, and data channels — organizations now talk about data infrastructure as a first-class architectural concern. Investment in data infrastructure has become a strategic conversation at every level of the business, not just within engineering teams.

It is in this context that Looker, the business intelligence (BI) and analytics platform acquired by and now part of Google Cloud, has shipped a release focused on what the company describes as foundational optimisation. The update targets application builders and developer services specifically, with upgrades spanning the development environment, marketing analytics integrations, and a new data discovery tool.

Looker Blocks for Google Marketing Platform

One of the headline additions is a set of new Looker Blocks for Google Marketing Platform. Looker Blocks are pre-built data models and dashboards designed to accelerate analytics development by providing a reusable, shareable foundation.

These new Blocks are purpose-built for Google Marketing Platform advertising and web data, and include:

• Interactive data exploration capabilities out of the box.
• Light machine learning (ML) predictions baked into the data models, enabling marketers to surface predictive insights without building custom ML pipelines.
• Direct links back to Google Marketing Platform, allowing analysts to act on insights by adjusting campaigns without leaving their analytics workflow.
• Paths for pushing insights back into Google Marketing Platform, enabling a feedback loop between analysis and campaign execution.

Colin Zima, head of Looker analytics and strategy at Google Cloud, has highlighted the ability to “customize data experiences” as central to this release, with deeper Google Cloud integration enabling solutions that were not previously practical.

Redesigned IDE for Model Developers

Looker’s built-in Integrated Development Environment (IDE) — used for authoring LookML data models — has been redesigned to improve developer workflows. Key improvements include:

• More intuitive workflows with advanced controls surfaced in a cleaner interface.
• An object browser that simplifies navigation across a project, making it easier to find, track, and reuse existing LookML objects such as views, explores, and dimensions.
• A single shared repository that can be shared across Looker instances, enabling teams to collaborate on model development without duplicating work.
• Project folders to improve organisation and make collaboration across large LookML projects more manageable.

Together, these changes aim to reduce the friction involved in building and maintaining complex LookML models, particularly in enterprise environments where multiple teams share a single Looker instance.

Developer Data Dictionary

Available through the Looker Marketplace, the new Data Dictionary is an application built using Looker’s extension framework. It provides a dedicated, purpose-built user interface that helps business users locate the fields and metrics they need for analysis — reducing their dependence on developers or data engineers for routine discovery tasks.

From a developer and governance perspective, the Data Dictionary also provides:

• The ability to audit all LookML fields across a project quickly.
• Visualisation of relationships between datasets and explores, helping teams understand data lineage and dependencies.
• Insight into which explores are used most frequently and which are redundant or duplicative — useful for maintaining a lean, well-governed LookML project.

Extension Framework Now Generally Available

Perhaps the most significant long-term development is the general availability of Looker’s extension framework. First announced in November as part of the Looker 7 release, the framework allows developers to build fully custom data experiences within Looker and embed those experiences into existing enterprise applications and workflows.

This positions Looker not just as a standalone BI tool but as a data platform and application development environment. Teams can build bespoke internal tools — dashboards, data explorers, self-service reporting apps — powered by Looker’s semantic layer and embedded directly into the products their users already use.

The Data Dictionary described above is itself an example of an application built on this framework, demonstrating the kind of tooling that becomes possible when analytics infrastructure is treated as a developer platform.

Key Takeaways for DevOps and Data Engineering Teams

• The redesigned LookML IDE reduces development friction for teams managing large or complex data models.
• Looker Blocks for Google Marketing Platform offer a reusable analytics foundation for organisations already invested in the Google advertising ecosystem.
• The Data Dictionary app improves data governance and self-service discovery, reducing analyst dependence on engineering teams.
• The generally available extension framework opens the door to treating Looker as an embedded analytics platform, not just a reporting tool.
• Shared repositories across instances are a meaningful improvement for organisations running multi-instance Looker deployments.

The Growing Need for DevOps in Healthcare

Healthcare IT teams are operating under significant pressure. Smaller budgets, staffing shortages, and the explosive growth of telehealth services have combined to make standardized processes and automation no longer optional — they are operational necessities. According to research from Redgate, the COVID-19 pandemic acted as a forcing function, compelling healthcare organizations to rapidly build and deliver telehealth platforms while simultaneously managing secure access to electronic health records (EHRs) and other sensitive patient data.

These conditions have accelerated interest in both application-level DevOps and — critically — database DevOps, an extension of DevOps principles into the database development and deployment lifecycle.

“We know that the healthcare sector is facing unprecedented demands from the pandemic and other regulatory struggles, and DevOps is the best way to tackle these issues. By then extending those practices into the database, the healthcare sector can not only improve the quality and efficiency of these systems, but ensure patient data remains secure and maintain compliance practices.”

— Kendra Little, DevOps Advocate, Redgate

Key Findings: Database DevOps Adoption in Healthcare

Redgate’s research surfaced several notable trends specific to the healthcare sector. The figures below are drawn from the published survey data and should be interpreted as directional indicators rather than definitive benchmarks.

1. Overall DevOps Adoption Is Ahead of Other Sectors

Seventy percent of healthcare respondents reported that they have adopted DevOps across all or some projects, or have at least a Proof of Concept (PoC) in place. This compares favorably to a 68% average across all sectors surveyed. More significantly, 41% of healthcare respondents have moved beyond the PoC stage into active adoption — compared to 36% in sectors such as government and financial services. This suggests that healthcare organizations, despite their compliance complexity, are not lagging behind in DevOps maturity.

2. Automation Adoption Is a Competitive Differentiator

Healthcare is outpacing other industries when it comes to adopting automation throughout the database development process. Survey respondents cited three primary motivators for automation investment:

• Free up developer time — cited by 27% of respondents
• Increase speed of delivery — cited by 22%
• Reduce the risk of data loss — cited by 13%

The prominence of data-loss risk reduction as a driver is particularly telling. In healthcare, a database error is not merely a technical inconvenience — it can directly impact patient safety and regulatory standing. Automation reduces the manual touchpoints where errors are most likely to occur.

3. Cloud Migration Is Underway but Not Yet Dominant

Forty-two percent of healthcare respondents reported using the cloud in some capacity for their database workloads. While this reflects significant progress, healthcare has historically been cautious about cloud adoption due to HIPAA and other regulatory requirements around data residency and security. The expectation at the time of this research was that cloud adoption would accelerate as remote work and telehealth services increased reliance on scalable, cloud-native infrastructure.

4. Version Control Is Being Used as a Compliance Foundation

Fifty-eight percent of healthcare respondents reported having version control measures in place for their database code, enabling automated builds and deployments. This is a meaningful compliance enabler: version control creates an auditable, repeatable development process that reduces the risk of unauthorized or unintended changes reaching production databases containing protected health information (PHI). For regulated industries, the ability to demonstrate that every change was reviewed, approved, and traceable is central to audit readiness.

5. Database Deployment Frequency Is Rising Sharply

One of the most concrete indicators of DevOps maturity is deployment frequency. In the 2020 data, 51% of healthcare respondents reported deploying database changes daily or weekly — a significant jump over prior years. Among North American respondents specifically, 55% reported deploying database changes daily, weekly, or on demand.

“What’s surprising this year is the way North American respondents are surging ahead, with 55% saying that they deploy database changes daily, weekly or even on demand. This indicates that they’re embracing Agile practices to better meet the needs of doctors, patients and remote services like telehealth that rely on these databases each day.”

— Kendra Little, DevOps Advocate, Redgate

Why Database DevOps Matters in Healthcare Specifically

Traditional DevOps conversations in healthcare tend to focus on application code — the APIs, front-end portals, and integration layers that connect systems. But databases are where the most sensitive and operationally critical data lives. EHR systems, claims databases, lab result repositories, and prescription management platforms all depend on the integrity and availability of their underlying databases.

Database DevOps addresses several healthcare-specific challenges:

• Compliance and auditability: Automated pipelines with version-controlled database changes provide the audit trail that HIPAA and other regulations require.
• Reducing human error in production: Manual database changes are a leading cause of outages and data integrity issues. Automated, tested deployments reduce this risk.
• Faster response to clinical requirements: Telehealth platforms and patient portals need to evolve quickly. Database DevOps removes bottlenecks that slow down schema changes and data migrations.
• Consistent environments: Development, staging, and production parity — a core DevOps principle — is especially important when debugging issues that involve patient data.

Takeaways for Healthcare Engineering Teams

If your organization is still treating database changes as a separate, manually-managed process outside your CI/CD pipeline, the data above suggests you are behind where your peers are heading. Practical first steps include:

1. Bring database scripts into version control alongside application code. Tools like Redgate SQL Source Control, Liquibase, and Flyway make this tractable for most database platforms.
2. Integrate database changes into your CI pipeline so that schema migrations are tested automatically on every pull request.
3. Automate compliance checks — static analysis and policy enforcement can flag changes that would introduce risk before they reach production.
4. Track deployment frequency as a metric. If your team cannot deploy database changes at least weekly without a manual change-control process that spans days, that is a signal to examine your pipeline.

The healthcare sector’s DevOps journey is accelerating. For database teams still operating in isolation from their application counterparts, the window to modernize — before compliance requirements or competitive pressures force a reactive overhaul — is narrowing.

Oracle Cloud’s newly launched VMware service takes an architectural approach that puts the channel front and center in selling, implementation and management, Ross Brown, vice president for go-to-market at Oracle Cloud Infrastructure, told CRN.

The market has been flooded with hyperscalers offering their versions of hosted VMware environments in recent years. But Oracle VMware Cloud Solution is unique in its ability to seamlessly operate alongside all types of workloads while empowering partners with full operational control, said Brown, a former VMware channel chief.

“In cloud, I have to serve all my IT: VMware workloads, VDI, cloud-native stuff. All those things have to work there, and those things are by definition partner integrated and delivered,” Brown said.

[Related: Oracle Cloud Preparing To Introduce Nvidia’s New A100s]

Oracle VMware Cloud Solution launched Thursday in all of Oracle’s global cloud regions, as well as through its on-premises cloud solution, Dedicated Region Cloud@Customer. The service, unveiled before last year’s Oracle OpenWorld conference, had previously been available only to beta customers through a limited release.

Oracle’s approach to delivering a native VMware service was grounded in the belief that hybrid and multi-cloud will become the norm for most enterprises, and the granular workings of hosted VMware environments should be entirely accessible and seamlessly interoperable with other workloads.

In recent years, VMware has forged alliances with the public cloud providers it once competed with, starting with Amazon Web Services and then moving on to Microsoft Azure and Google Cloud Platform.

Oracle didn’t want to emulate any of those plays, Brown said.

Instead, the Redwood Shores, Calif.-based tech giant focused on the question: “How do you build a more performant cloud that actually allows partners to engage on their workloads and go run them?” Brown said.

Microsoft Azure and Google Cloud offer dedicated hardware to run VMware that customers can connect to the rest of their cloud instances. And VMware Cloud on AWS also stands apart from the native AWS environment, Brown said.

“AWS doesn’t offer VMware. VMware offers VMware on AWS. You can build a connection back to your AWS tenancy, but it’s still separate,” he told CRN.

Oracle took a different tack, integrating the full VMware stack—vSphere, NSX network virtualization, vSAN storage virtualization, vRealize management—directly into the same bare-metal clusters that comprise the rest of the customer’s cloud tenancy.

The comprehensive integration of OCI with the VMware Cloud Foundation stack and VMware tools was an endeavor that Oracle product and engineering teams worked on for two years.

“VMware is a pretty detailed infrastructure environment,” Brown said. “It maximizes the hardware. It’s definitely not a lightweight tool, and it operates at a very thin layer.”

That approach, while technically challenging, delivers certain advantages—especially to the channel.

For starters, all VMware and third-party tools that partners currently use to manage on-premises VMware environments will work just the same in Oracle’s cloud.

“They operate identically because we’re running the same physical infrastructure, just as a cloud service,” Brown said.

With granular control over nearly all infrastructure operations, VMware partners can offer a complete catalog of managed services, just as they would if they stood up vSphere in their own data centers. Oracle allows them to create a VMware region and offer their customers shared tenancy, then use the complete set of MSP tools that VMware distributes through its VMware Cloud Provider Program.

“The notion of moving on-premises hardware into a cloud environment, still have everything work the same way, and have all management contracts go along with it, is pretty powerful,” Brown said.

Oracle also is the first cloud to allow customers to select a version of the ESX hypervisor, which is useful for those with apps certified for specific ESX releases.

“If I want to get under the hypervisor and do things with vCloud Director, we’re the only cloud that can do that,” he said.

Oracle’s partnership with VMware reflects its larger philosophy of cloud being an approach, not a place, Brown said.

But Oracle certainly hasn’t yet built a reputation as a VMware provider—the companies are better known for a longstanding rivalry predating their cloud transformations.

That’s another reason Oracle set out to build an offering that empowers the channel, Brown said, as it needs partners to be the trusted advisers that bring the service to enterprise customers.

“I‘m glad to see more native support from Oracle on their customers’ platforms of choice,” said David Klee, founder and chief architect at VMware partner Heraflux.

A native VMware platform on Oracle Cloud will further encourage the channel to migrate Oracle databases out of on-premises data centers and into the cloud, Klee told CRN.

“I‘m eager to see how this cloud platform continues to evolve and expand, and what other service offerings they will add in the future to complement this big shift in their model,” he said.

The product that appears to be stimulating the most customer interest for Oracle VMware Cloud Solution is Dedicated Region Cloud@Customer, Brown said. The on-premises infrastructure unveiled in June offers the same array of services, SLAs and pricing models as Oracle’s public cloud.

On-premises Exadata hardware running cloud-native apps built with Kubernetes and legacy ones built on VMware is a compelling offer to many large Oracle customers, Brown told CRN.

Oracle Executive Chairman and CTO Larry Ellison praised the solution last September in an OpenWorld keynote.

“We have a very important relationship with VMware,” Ellison said. ”The VMware stack you‘re running on-premises, you really can lift and shift it intact to the Oracle Cloud.”

Verizon Partners with Google Cloud to Modernize Customer Contact Centers

Verizon announced a pilot program to deploy Google Contact Center Artificial Intelligence (CCAI) with the goal of delivering more intuitive, efficient customer experiences across voice and chat channels. The partnership reflects a broader push by Google Cloud to position its AI tooling as infrastructure for telecom and enterprise contact centers.

How Google Contact Center AI Works

At the core of the offering is natural language understanding (NLU), which allows customers to state or type their requests in plain language rather than navigate layered IVR menus. When a customer contacts Verizon by phone or direct chat, the AI interprets the intent and routes or resolves accordingly — reducing friction at the first point of contact.

According to a Google Cloud statement cited in the original report, the technology is intended to “help empower shorter call times, quicker resolutions, and improved outcomes for customer satisfaction.”

When a call escalates to a human agent, Google CCAI continues to contribute by:

• Providing real-time agent assist — surfacing relevant knowledge base articles and suggested responses during the live interaction
• Locating relevant customer history and context automatically
• Reducing the cognitive load on agents so they can focus on complex or sensitive issues

The Role of Dialogflow

Much of the experience is powered by Dialogflow, Google Cloud’s platform for building conversational agents across voice, chat, and messaging channels. Unlike older bot frameworks that modeled human conversations as strictly linear flows, Dialogflow is designed to handle multi-turn, dynamic conversations — more closely approximating how human agents actually interact with customers.

Dialogflow supports integration across a wide range of channels and can manage multiple concurrent requests, which is critical for high-volume contact center environments.

Industry Analyst Perspective

Irwin Lazar, Vice President and Service Director at Nemertes Research, told UC Today that the deal is a logical fit, noting that many contact center platform providers already integrate Google Cloud’s contact center technology into their own offerings. This means Verizon is building on a technology foundation that has already achieved meaningful adoption across the industry.

Conversational AI Will Augment — Not Replace — Human Agents

Seth Miller, CEO of VerbalWare and a chatbot industry expert, offered a measured take on the partnership’s potential. He described the Verizon–Google deal as “a win all around,” particularly in the context of the COVID-19 pandemic, which strained customer service teams across industries while simultaneously raising customer expectations.

“COVID has, and will continue to be, a wake-up call for the economy. Customer service teams have shrunk, but customers expect a superlative experience.”

Seth Miller, CEO, VerbalWare

Miller argued that conversational AI will not fully replace human agents but can absorb a large volume of routine, repetitive queries — freeing agents to handle higher-complexity interactions that genuinely require human judgment. He also stressed that companies deploying these systems will need to invest in retraining agents to design conversation flows and work alongside NLU platforms effectively.

Broader Google Cloud Enterprise Momentum

The Verizon announcement was part of a wider wave of enterprise commitments to Google Cloud. Other organizations reported to have adopted Google Cloud technology around the same period include Deutsche Bank, Goldman Sachs, and Telefónica — one of the world’s largest telecom operators by subscriber count.

At Google Cloud Next ’20, Google also announced BigQuery Omni, a multi-cloud analytics solution built on the Anthos hybrid and multi-cloud platform. BigQuery Omni allows users to run analytics workloads against data stored across multiple cloud environments using a consistent interface — addressing a real operational challenge for enterprises with data distributed across AWS, Azure, and Google Cloud.

“We’re honored to see organizations across every industry place their trust in Google Cloud, as we partner to solve real-world business problems.”

Thomas Kurian, CEO, Google Cloud

Key Takeaways for DevOps and Platform Engineers

• Dialogflow is the underlying conversational AI platform. If you’re evaluating NLU frameworks for contact center or chatbot use cases, it’s worth assessing alongside alternatives such as Amazon Lex and Microsoft Bot Framework.
• Google CCAI is designed to integrate with existing telephony infrastructure, not replace it wholesale — making incremental adoption feasible.
• Real-time agent assist is increasingly table-stakes for enterprise contact center AI — evaluate vendors on the quality of their suggestion latency and knowledge retrieval accuracy.
• BigQuery Omni signals Google’s intent to compete for analytics workloads even when data lives outside GCP — a significant architectural consideration for multi-cloud strategies.
• Workforce planning matters: AI deployment in contact centers requires parallel investment in agent training and conversation design skills, not just technology procurement.

1. Plan for Enterprise Readiness

No enterprise DevOps initiative succeeds without visible, committed sponsorship from senior leadership. Before writing a single pipeline script, identify an executive sponsor—typically a CIO, CTO, or a senior business-line owner—who can allocate budget, remove organizational blockers, and signal to the rest of the company that this transformation matters.

Enterprise readiness planning should address:

• Stakeholder alignment: Business, development, operations, and security teams must all have a seat at the table from day one. DevOps cannot succeed as a purely technical project.
• Current-state assessment: Audit existing workflows, toolchains, and infrastructure to understand what can be modernized incrementally and what requires replacement.
• Compliance mapping: In regulated industries (finance, healthcare, government), map compliance requirements—audit trails, change-management controls, access restrictions—into the target pipeline architecture before you build it.
• Phased rollout plan: Define pilot teams or value streams where you will prove the model before scaling across the organization.

Design-thinking workshops can be an effective way to align cross-functional stakeholders on the problem space and agree on what “done” looks like before any tooling decisions are made.

2. Emphasize Agile Principles

Agile and DevOps are related but distinct disciplines. Agile focuses on iterative product development and team collaboration; DevOps extends that thinking to include continuous delivery and operational feedback loops. When applied together, they create a delivery system that is both responsive to change and operationally stable.

Key agile practices that reinforce an enterprise DevOps strategy include:

• Short iteration cycles: Sprints or continuous-flow cadences keep work-in-progress small and feedback loops tight.
• Continuous Integration (CI): Every code commit triggers an automated build and test run, catching integration issues early.
• Continuous Deployment (CD): Validated artifacts move automatically through staging environments, reducing manual handoffs and accelerating time-to-market.
• Cross-functional teams: Embedding operations and security engineers alongside developers removes the wall that DevOps is specifically designed to eliminate.

At the enterprise level, frameworks such as SAFe (Scaled Agile Framework) or LeSS (Large-Scale Scrum) provide structures for coordinating multiple agile teams while preserving alignment with business objectives.

3. Integrate a Container Registry

Containerization has become a cornerstone of modern DevOps pipelines, and a well-managed container registry is essential for enterprise-scale image governance. A registry acts as the single source of truth for container images, controlling what gets promoted from development through staging to production.

When evaluating a container registry for enterprise use, look for the following capabilities:

• Vulnerability scanning: Automated scanning of image layers against known CVE databases catches security issues before images reach production.
• Access control: Role-based permissions ensure that only authorized teams can push or pull specific images or image tags.
• Artifact promotion policies: Define rules that govern when an image can move from a development repository to a staging or production repository, enforcing quality gates automatically.
• API and CLI integration: REST APIs and command-line tooling allow the registry to integrate cleanly with CI/CD pipelines, whether you are using Jenkins, GitHub Actions, GitLab CI, or another platform.
• Scalable storage: Enterprise workloads generate large numbers of image versions; the registry must handle this at scale, either on-premises or via a cloud-hosted service.

Popular enterprise registry options include JFrog Artifactory, Harbor (open source), AWS ECR, Google Artifact Registry, and Azure Container Registry. The right choice depends on your existing cloud footprint and on-premises requirements.

4. Automate Testing Procedures

Automated testing is the quality gate that makes continuous delivery safe. Without it, the speed gains from CI/CD pipelines simply move defects to production faster. A mature enterprise testing strategy layers multiple test types across the pipeline:

• Unit tests: Fast, isolated tests that validate individual functions or classes. These should run on every commit and complete in seconds.
• Integration tests: Verify that services and components interact correctly. These take longer but catch interface-contract issues before deployment.
• End-to-end (E2E) tests: Simulate real user journeys through the full application stack. Run these on release candidates, not every commit.
• Security scanning (SAST/DAST): Static Application Security Testing analyzes source code; Dynamic Application Security Testing probes a running application. Both should be automated pipeline stages, not manual periodic reviews.
• Performance and load tests: Prevent regressions in throughput and latency before changes reach production.

Automated tests can run in cloud-hosted environments (provided by the target cloud platform or third-party services) or in on-premises infrastructure. The critical requirement is that the pipeline controls when an artifact is promoted: a build that fails any mandatory test gate must not advance to staging or production, regardless of release pressure.

Test data management is often an underestimated challenge in enterprise environments. Establish processes to generate, anonymize, and provision realistic test data so that automated tests exercise meaningful scenarios without exposing production data.

5. Communicate Across the Organization

Technology and process changes will stall without deliberate internal communication. People resist change they do not understand—especially in large organizations where DevOps transformation can feel like a threat to established roles and workflows.

Effective organizational communication strategies include:

• Explain the “why” clearly: Connect the DevOps strategy to business outcomes that resonate—faster feature delivery, fewer outages, improved security posture—not just technical benefits.
• Create regular touchpoints: Team briefings, engineering newsletters, internal wikis, and demo days keep the wider organization informed and reduce rumor-driven anxiety.
• Build communities of practice: Cross-team forums where engineers share learnings, tooling tips, and post-incident reviews accelerate organizational learning and reduce siloed experimentation.
• Celebrate wins publicly: When a team ships faster or reduces incident frequency after adopting a new practice, highlight that success to motivate others.
• Provide training pathways: Identify skill gaps created by new tooling or practices and invest in training programs, certifications, or pairing opportunities to close them.

Communication is not a one-time launch announcement. It must be a continuous, bidirectional flow that also surfaces concerns from teams on the ground back to leadership.

Putting It All Together

Deploying an enterprise DevOps strategy for agile administration is as much a cultural and organizational challenge as it is a technical one. The five steps above—planning for enterprise readiness, grounding the effort in agile principles, establishing reliable container image governance, automating quality gates throughout the pipeline, and communicating openly across the organization—form a coherent framework rather than an unordered checklist.

Start with a well-scoped pilot, demonstrate measurable value, and use that proof point to build the organizational confidence needed to scale. Enterprise DevOps transformations rarely succeed as big-bang rewrites; they succeed as a series of deliberate, well-communicated improvements compounded over time.