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.

Originally published by Heroku, the methodology was designed specifically to support Software-as-a-Service offerings. In this article we walk through each of the 12 factors and show how Eclipse MicroProfile and Kubernetes provide concrete tools to satisfy them.

Why the 12-Factor App?

Modern cloud-native applications must handle frequent deployments, elastic scaling, and diverse runtime environments. The 12-Factor App methodology answers these challenges with a set of best practices that help teams:

• Use declarative formats for setup automation, reducing onboarding time for new developers.
• Maintain a clean contract with the OS, maximising portability across execution environments.
• Design for deployment on modern cloud platforms, removing the need for bespoke server administration.
• Keep development and production as similar as possible, enabling continuous deployment.
• Scale horizontally without major changes to tooling, architecture, or development practices.

Eclipse MicroProfile: A Brief Overview

Eclipse MicroProfile is an open-source project and community established by the Eclipse Foundation in 2016. Its members include IBM, Red Hat, Tomitribe, Payara, the London Java Community (LJC), and many others. MicroProfile targets lightweight, iterative development with a rapid release cadence—typically three releases per year.

The project has numerous runtime implementations, including Open Liberty, Quarkus, Payara, TomEE, WildFly, KumuluzEE, Piranha, and others. Each implementation provides the MicroProfile APIs on top of a Jakarta EE or compatible base.

The 12 Factors, One by One

1. Codebase

One codebase tracked in version control, many deploys.

Use a dedicated Git repository per microservice so that each service can evolve and be released independently. Version control enforces discipline: maintaining a single repository per application forces teams to analyse the boundaries of their system and identify parts of a potential monolith that should be extracted into separate microservices.

2. Dependencies

Explicitly declare and isolate dependencies.

Avoid bundling third-party libraries inside your packaged artifact. Instead, declare them explicitly in a build descriptor—pom.xml for Maven or settings.gradle / build.gradle for Gradle. This approach makes version upgrades predictable and keeps the artifact lean.

3. Config

Store configuration in the environment.

The golden rule is: write once, configure everywhere. Configuration that changes between deployments (database URLs, credentials, feature flags) must be externalised from application code so that you never need to repackage a service just to change a property.

MicroProfile Config lets you read properties from system properties, environment variables, or custom config sources such as a Kubernetes ConfigMap—without changing application code.

Two access patterns are supported:

// Programmatic lookup
Config config = ConfigProvider.getConfig();
String value = config.getValue("my.string.property", String.class);

// CDI injection
@Inject
@ConfigProperty(name = "my.string.property")
String myPropV;

Because the property can be backed by a Kubernetes ConfigMap or a plain environment variable, updating it does not require rebuilding or redeploying the container image.

4. Backing Services

Treat backing services as attached resources.

Databases, message brokers, and external APIs should be swappable by changing configuration alone. MicroProfile Rest Client lets you model a backing service as a typed Java interface annotated with @RegisterRestClient, and then bind its URL via a config property.

// 1. Declare the interface
@Dependent
@RegisterRestClient
@RegisterProvider(UnknownUrlExceptionMapper.class)
@Path("/properties")
public interface SystemClient {
    @GET
    @Produces(MediaType.APPLICATION_JSON)
    Properties getProperties() throws UnknownUrlException, ProcessingException;
}

// 2. Inject it where needed
@Inject
@RestClient
private SystemClient defaultRestClient;

// 3. Bind the URL in config (e.g., microprofile-config.properties or ConfigMap)
// com.acme.SystemClient/mp-rest/url=http://localhost:9080/system

Swapping the backing service only requires updating the binding property value, provided the replacement service implements the same interface.

5. Build, Release, Run

Strictly separate build and run stages.

• Build stage: Compile source code and produce an artifact — for example, mvn package produces a JAR.
• Release stage: Combine the build artifact with environment-specific configuration and produce a versioned container image — for example, docker build followed by pushing to a registry.
• Run stage: Execute the immutable release image in the target environment — for example, docker run locally or kubectl apply -f deployment.yaml in Kubernetes.

Each release should be uniquely identified and immutable. You never modify a running release; you create a new one.

6. Processes

Execute the app as one or more stateless processes.

Microservices should be stateless so that the infrastructure can destroy and recreate instances without data loss. REST is a widely adopted stateless protocol. JAX-RS, included in MicroProfile, makes it straightforward to build RESTful services:

@ApplicationPath("system")
public class SystemApplication extends Application {}

@Path("properties")
public class PropertiesResource {
    @GET
    @Produces(MediaType.APPLICATION_JSON)
    public JsonObject getProperties() { /* ... */ }
}

This exposes the endpoint http://host:port/system/properties as a stateless RESTful service. Any persistent state belongs in a backing service (factor 4), not in process memory.

7. Port Binding

Export services via port binding.

In cloud environments, service ports change frequently. MicroProfile Config combined with MicroProfile Rest Client handles this cleanly. Store the service URL in a Kubernetes ConfigMap and reference it via the mp-rest/url config property. MicroProfile Config picks up the value automatically at runtime.

Note that Kubernetes environment variable names cannot contain dots (.) or forward slashes (/). Replace non-alphanumeric characters with underscores when mapping property names to environment variables. For example:

# Property name
com.acme.SystemClient/mp-rest/url=http://service-host:9080/system

# Equivalent environment variable name
com_acme_SystemClient_mp_rest_url=http://service-host:9080/system

8. Concurrency

Scale out via the process model.

Stateless microservices can be scaled horizontally by running more instances. Kubernetes Horizontal Pod Autoscaler (HPA) and Knative autoscaling can scale deployments up or down automatically based on observed metrics such as CPU utilisation or incoming request rate.

9. Disposability

Maximise robustness with fast startup and graceful shutdown.

Microservices should start quickly and shut down gracefully. They also need to be resilient to transient failures in downstream services. MicroProfile Fault Tolerance provides a set of annotations that make services more robust:

• @Retry — automatically retry on transient network failures.
• @Timeout — fail fast if a response takes too long.
• @CircuitBreaker — stop calling a failing service to give it time to recover.
• @Bulkhead — limit concurrent calls to prevent one slow service from exhausting all threads.
• @Fallback — execute an alternative method when the primary path fails.

The example below shows a JAX-RS endpoint that will respond within 100 ms regardless of back-end latency, retrying on failure and falling back to a default response if necessary:

@Path("/client")
@ApplicationScoped
public class ClientController {

    @Inject @RestClient
    private Service service;

    @Inject @ConfigProperty(name = "conference")
    String conf;

    @GET
    @Path("/test/{parameter}")
    @Retry
    @Timeout(100)
    @Asynchronous
    @Fallback(fallbackMethod = "fallback")
    public CompletionStage<String> onClientSide(@PathParam("parameter") String parameter) {
        return CompletableFuture.completedFuture(
            "We are live @ " + conf + "! " + service.doSomething(parameter)
        );
    }

    public CompletionStage<String> fallback(@PathParam("parameter") String parameter) {
        return CompletableFuture.completedFuture("This is my fallback!");
    }
}

10. Dev/Prod Parity

Keep development, staging, and production as similar as possible.

Differences between environments are a leading cause of subtle bugs. Container images built with Docker eliminate the classic “it works on my laptop” problem: the same image runs in a developer’s local environment, in CI, in staging, and in production. Kubernetes manifests further codify the expected runtime configuration so that every environment is defined as code.

11. Logs

Treat logs as event streams.

Applications should write log events to standard output (stdout) and let the execution environment handle routing and storage. In Kubernetes, logs written to stdout/stderr are captured by the container runtime and can be forwarded to centralised log aggregation platforms such as the ELK Stack (Elasticsearch, Logstash, Kibana) or Loki. Emitting logs in structured JSON format makes them easier to query and index.

12. Admin Processes

Run admin and management tasks as one-off processes.

Database migrations, cache warming, and similar administrative tasks should run as separate, short-lived processes rather than being embedded in the main application. Kubernetes Jobs are a natural fit: a Job runs a container to completion and then terminates, keeping administrative logic cleanly separated from service logic.

Summary

The table below maps each of the 12 factors to the primary MicroProfile or Kubernetes feature that addresses it:

Eclipse MicroProfile and Kubernetes together provide a comprehensive toolkit for implementing all 12 factors. MicroProfile handles application-level concerns—configuration, service clients, fault tolerance, and RESTful APIs—while Kubernetes provides the runtime infrastructure for scheduling, scaling, secrets management, and job execution.

Original article sourced from msdynamicsworld.com (2020). Code examples are illustrative and based on the MicroProfile API specifications.

Rubrik’s Case for Unifying Data Management and DevOps

At its FORWARD Digital Summit event, Rubrik made a public case for converging data management and DevOps practices. While the company is best known as a data protection vendor, president Dan Rogers positioned Rubrik as a broader data management platform provider — one whose API-first architecture could soon make it a first-class tool for DevOps teams, not just IT operations.

New Cloud Coverage: GCP, Amazon RDS, and OneDrive

Rubrik announced several platform expansions to its Polaris Data Management Platform, delivered as a SaaS offering:

• Google Cloud Platform (GCP): Polaris can now protect workloads running on virtual machines in GCP, adding to existing support for Amazon Web Services (AWS) and Microsoft Azure.
• Amazon Relational Database Service (Amazon RDS): AWS data protection support was extended to cover managed relational databases running on RDS.
• Microsoft OneDrive: Cloud file storage on OneDrive is now covered, complementing the existing Microsoft Office 365 integration.

These additions bring Rubrik closer to comprehensive multi-cloud data protection across the three dominant cloud providers and their managed services.

Polaris as an API-First Platform for DevOps

The more strategically significant announcement at the summit was Rubrik’s articulation of how Polaris fits into DevOps workflows. According to Rogers, Polaris is built from the ground up using REST APIs and rich metadata, making it programmatically accessible — a key requirement for DevOps-style automation.

Today, most Polaris users are IT operations teams accessing it through a graphical user interface (GUI). That GUI-driven approach allows the platform to serve traditional IT operations while also being accessible to DevOps teams who want to automate data access and protection through code. Rogers suggested it is only a matter of time before DevOps teams start invoking Polaris APIs directly to access the large volumes of data their applications depend on.

Data Protection as a DevOps Enabler

Rogers highlighted two specific ways data protection platforms already support application development cycles:

• Development and testing environments: Developers need rapid recovery capabilities when an application crashes mid-development. A robust backup and restore platform reduces the cost of failure during iterative development cycles.
• Cloud migration: IT teams are actively using data protection and backup tooling to migrate datasets from on-premises environments into the cloud — a use case that aligns naturally with DevOps-driven modernization initiatives.

Beyond Backup: Data Governance, Compliance, and Metadata

Rubrik’s broader platform strategy extends beyond backup and recovery into adjacent data management capabilities, including:

• Data governance and compliance tooling
• Metadata management
• SaaS delivery of traditionally on-premises data management functions

Rubrik is not alone in this space; other vendors also deliver data management capabilities as SaaS. However, Rubrik’s pitch is a phased replacement of on-premises data protection with a cloud-native alternative that gradually adds higher-order management capabilities.

COVID-19 as an Accelerant for SaaS Adoption

Rogers also addressed the impact of the COVID-19 pandemic on IT platform decisions. With IT operations teams working remotely for an extended — and in some cases potentially permanent — period, organizations are accelerating their shift toward cloud-delivered management platforms that can be accessed from anywhere without requiring physical presence in a data center.

This trend benefits SaaS-delivered platforms like Polaris directly. The pandemic effectively compressed a multi-year adoption timeline into months, as organizations that had deferred cloud migrations were pushed to act by the operational realities of distributed teams.

Key Takeaways for DevOps and Platform Engineers

• Rubrik’s Polaris platform now covers all three major clouds (AWS, Azure, GCP) for VM workload protection.
• Amazon RDS and Microsoft OneDrive are newly supported data sources.
• The REST API-first architecture of Polaris positions it as an automation target for DevOps pipelines — though widespread DevOps adoption of data management APIs remains nascent.
• Data protection platforms increasingly serve dual roles: operational resilience for IT teams, and data access infrastructure for development and testing workflows.
• Remote work pressures are accelerating SaaS platform adoption across IT operations broadly.

The Core Challenge: Too Much Data, Too Little Time

Modern IT teams operate under an uncomfortable contradiction. The systems they maintain have become business-critical — when apps go down, revenue disappears and customers churn — yet the sheer volume of telemetry data those systems generate has made manual monitoring impractical. Mainframes gave way to distributed computing, which gave way to virtualisation, which gave way to microservices and ephemeral containers. Each transition multiplied the number of signals an operations team must interpret.

Legacy monitoring tools were designed for simpler, more static environments. Today they produce an overwhelming flood of alerts, most of which are noise, while the genuinely actionable signals are buried underneath. The result: teams spend more time reacting to emergencies than building new capabilities, agility stalls, and innovation shrinks.

How DevOps Tried to Solve the Problem

The DevOps movement tackled this challenge by re-centering ownership. Rather than handing off services to a centralised operations team with little product context, DevOps places responsibility for reliability, deployment, and support directly on the engineers who write the code. This decentralises operations into smaller, empowered squads, each accountable for its own microservice and its contribution to the overall customer experience.

Many organisations complement this model with a Site Reliability Engineering (SRE) function. SREs monitor observability data, identify potential failure modes early, and feed those insights back into the development cycle — strengthening reliability and scalability before incidents reach production.

DevOps and SRE practices represent genuine progress. However, they surface a new set of coordination problems as organisations scale.

Where DevOps Teams Still Struggle

Even well-run DevOps organisations encounter friction at the boundaries between teams and tools:

• Alert noise at scale. Each microservice emits its own telemetry. Across dozens or hundreds of services, surfacing the events that actually matter — and understanding how they relate to each other — requires significant cognitive effort.
• Unclear blast radius. When an incident occurs, understanding which downstream services and customers are affected is rarely straightforward. Teams need relationship context, not just raw metrics.
• Cross-team coordination. DevOps squads interact primarily through APIs. When a multi-service incident demands urgent collaboration from geographically dispersed teams with different on-call schedules and escalation paths, orchestrating the right response quickly is genuinely hard.
• Post-incident learning. Without a structured feedback loop, teams repeat the same investigations and make the same mistakes. Post-mortems are often rushed or skipped under the pressure of the next deployment.

These are not failures of DevOps as a philosophy — they are the natural consequences of operating at a scale that exceeds what human attention alone can manage.

What Is AIOps?

Artificial Intelligence for IT Operations (AIOps) refers to platforms that apply machine learning and data analytics to the monitoring, event correlation, and incident response workflows that operations and SRE teams depend on. The term was popularised by analyst firm Gartner, though the underlying techniques — anomaly detection, topology mapping, noise reduction, root cause analysis — have been evolving for years.

A mature AIOps platform typically provides several capabilities:

• Baseline learning. The platform observes normal behaviour across your environment over time, so it can distinguish a genuine anomaly from expected variation.
• Alert correlation and noise reduction. Related alerts from multiple sources are grouped into a single, actionable incident rather than flooding dashboards with thousands of individual events.
• Root cause identification. By mapping dependencies between services and infrastructure components, AIOps can point to the probable origin of an incident rather than simply listing its symptoms.
• Prescriptive recommendations. Drawing on historical resolution data, the platform can suggest remediation steps, shortening the time from detection to fix.
• Intelligent notification routing. The right people are engaged at the right time, respecting on-call schedules, escalation paths, and geographic constraints.
• Collaborative workspaces. Team members share a consistent, real-time view of the incident, its context, and the actions being taken.
• Post-mortem acceleration. Structured capture of resolution steps and learnings feeds future incident prediction and speeds future response.

How AIOps Complements DevOps

DevOps and AIOps are not competing philosophies. AIOps is best understood as the intelligence layer that makes DevOps practices sustainable at enterprise scale.

Cutting Through the Noise

SREs and DevOps engineers should spend their attention on meaningful signals. AIOps handles the initial filtering — correlating alerts across tools and services, suppressing duplicates, and surfacing only the events that warrant human action. Engineers engage with context, not chaos.

Closing the Gap Between Detection and Resolution

By identifying probable root causes and suggesting remediation steps drawn from previous incidents, AIOps shortens mean time to resolution (MTTR). Teams do not start each incident investigation from scratch; they begin with a working hypothesis informed by machine learning applied to historical patterns.

Orchestrating Cross-Team Response

When an incident spans multiple services and requires engineers from multiple teams, AIOps can handle the coordination logic: who is on call, what their escalation path is, which communication channel they prefer. This removes a significant manual burden from incident commanders and reduces response lag.

Building Institutional Memory

Post-mortems generate valuable data that typically lives in a document nobody reads. AIOps platforms ingest resolution steps and feedback, making that knowledge available during future incidents through predictive analysis. Repetitive problems become detectable — and preventable — before they cause outages.

Enabling Edge-Based Intelligence

For AIOps to be effective in high-volume environments, anomaly detection should happen close to where data is generated — at the edge — rather than centralising every raw event before analysis. This reduces data transport costs and latency, and allows the platform to act on local patterns without waiting for a central aggregation step.

Practical Considerations for Teams Evaluating AIOps

Organisations considering an AIOps investment should evaluate platforms against a few practical criteria:

• Integration breadth. An AIOps platform is only as useful as the data it can ingest. Look for broad, well-maintained integrations with your existing monitoring, logging, tracing, ITSM, and communication tools.
• Time to value. Machine learning models need training data. Understand how long a platform takes to learn your environment before it produces useful signal, and what the experience looks like during that ramp-up period.
• Explainability. Engineers need to trust the platform’s recommendations. Opaque black-box correlations are harder to act on and harder to validate. Prefer platforms that can explain why they grouped events or suggested a remediation step.
• Feedback loops. The platform should improve as your team provides feedback — marking correlations as incorrect, flagging missed incidents, confirming root cause assessments. Without this loop, the model stagnates.
• Organisational readiness. AIOps adoption is a cultural and process change as much as a technical one. Teams need to trust automated triage enough to act on it, and to contribute structured feedback. Plan for enablement and change management.

The Bottom Line

DevOps represents a genuine and necessary evolution in how organisations build and operate software. But the scale of modern distributed systems — and the volume of observability data they produce — exceeds what human teams can manage without assistance. AIOps provides that assistance: not to replace engineers, but to remove the work that should never have required engineering judgement in the first place.

When AIOps handles noise reduction, correlation, root cause identification, and notification routing, DevOps and SRE engineers are freed to do what they do best: understand systems deeply, design for reliability, and build experiences that serve customers. That is the argument for DevOps and AIOps working together — not as competing strategies, but as complementary layers of a mature operations practice.

The Skills Crisis Hitting Enterprise IT

As enterprises accelerate adoption of cloud computing, hybrid IT architectures and DevOps practices, traditional departmental siloes are dissolving — but they are being replaced by a new set of pressures: a widening skills gap, strained training budgets and an incoming workforce that many senior practitioners believe is not yet ready for modern distributed environments.

The SolarWinds IT Trends Report 2020 — conducted just before the COVID-19 pandemic reshaped the operational context for IT teams globally — captures these pressures in sharp relief. Its findings highlight a workforce already stretched thin, facing unclear priorities and insufficient resources to keep pace with technology change.

How Enterprise Technology Roles Are Changing

When respondents were asked to identify the top ways their roles were evolving, three themes dominated:

• Retraining existing staff — cited by 43% of respondents as the primary driver of role change.
• Increased on-premises responsibilities — 40% reported expanding scope of infrastructure management, even as cloud adoption grows.
• Longer working hours — 37% said their working week has grown, indicating that headcount is not keeping pace with workload.

At the same time, IT professionals identified the biggest obstacles standing in the way of effective delivery:

• Lack of budget and resources — 36%
• Unclear or shifting priorities — 23%
• Insufficient training for personnel — 12%

These constraints compound each other: when budgets are tight, training is cut first, which deepens the skills gap, which in turn increases workload on experienced staff.

New Entrants Aren’t Ready for Modern IT Environments

A particularly striking finding concerns the readiness of new technology professionals entering the workforce. According to the survey:

• 48% of respondents said new hires do not arrive equipped with the skills needed to manage modern, distributed IT environments.
• Only 28% believe new entrants do possess the necessary skills.

This perception gap has practical consequences. When nearly half of hiring managers doubt the readiness of incoming talent, the burden of upskilling falls entirely on the organization — precisely when budgets are under pressure and teams are already stretched.

Soft Skills Are Now Table Stakes for DevOps Success

Technical capability alone is no longer sufficient in cross-functional DevOps and cloud environments. Both the SolarWinds and DevOps Institute surveys converge on the growing importance of interpersonal and organizational skills.

The SolarWinds survey identified the following non-technical competencies as increasingly critical:

• Project management — 69%
• Interpersonal communication — 57%
• People management — 53%

Yet despite recognizing the importance of communication, only 53% of respondents said they feel comfortable communicating with business leaders when requesting budget for technology purchases or staff training. This hesitancy can create a self-reinforcing cycle: teams that struggle to articulate business value are less likely to secure the investment needed to close their skills gap.

The DevOps Institute Upskilling 2020 report, based on 1,300 global respondents, reinforces this point: more than 50% of enterprises reported challenges managing the people and processes behind DevOps — not just the tooling — and 61% identified interpersonal skills as crucial to DevOps success.

Application Performance Management and Cloud Observability

As IT portfolios shift from on-premises hardware toward cloud and SaaS services, the observability and monitoring skill set required has expanded significantly. Key findings from the SolarWinds survey include:

• 54% of respondents now spend more time managing applications and services than infrastructure and hardware.
• When moving to cloud, 57% use network traffic and network application analysis tools, and 55% use user experience monitoring.
• 52% rely on log analysis for cloud visibility.
• For application performance management (APM), 61% use a combination of native cloud-vendor tools and third-party toolsets — reflecting the reality that no single vendor covers every observability need.

Enterprise-scale organizations show a notably higher reliance on advanced techniques: log analytics adoption reaches 58% at the enterprise tier, and distributed tracing is used by 24% of enterprise respondents — versus much lower rates at smaller organizations. By contrast, only 23% of small businesses reported having no plans to adopt tooling for cloud or SaaS visibility.

Security Management: A Growing Time Commitment

Security is not just a technical concern — it is now a primary time sink for IT professionals at all levels. According to the survey:

• 65% of IT professionals spend at least 10% of their working day on security management activities.

The top security skill areas organizations are prioritizing for development are:

• Network security — 48%
• Security information and event management (SIEM) — 33%
• Backup and recovery — 29%

The emphasis on backup and recovery alongside threat detection reflects a mature, defense-in-depth approach: organizations are investing not only in preventing breaches but in minimizing recovery time when they occur.

Regulatory Compliance Is Expanding IT Headcount Requirements

Compliance obligations are another factor driving up staffing and skills requirements. According to the SolarWinds survey, 46% of respondents said regulatory compliance has fueled the need for additional staff. The regulations with the biggest operational impact were:

• GDPR — 45%
• PCI DSS — 34%
• HIPAA — 34%

The breadth of regulation — spanning data privacy, payment card security and healthcare data — means that no single compliance specialization is sufficient. IT teams operating across industries must maintain working knowledge of multiple regulatory frameworks simultaneously, further straining already limited training budgets.

What IT Leaders Should Take Away

The compounding pressures captured by these surveys point to several practical priorities for IT leaders:

1. Budget for internal upskilling programs — With nearly half of hiring managers unconvinced that new entrants are ready, waiting to hire your way out of a skills gap is not a viable strategy. Structured internal training programs for cloud, observability, security and compliance competencies are increasingly necessary.
2. Treat soft skills as technical requirements — Project management, cross-team communication and business stakeholder engagement should appear in role descriptions and development plans, not just as nice-to-haves.
3. Invest in observability tooling that reduces cognitive load — As environments grow more complex, the right combination of native cloud monitoring and third-party APM tools can reduce the time experienced engineers spend on reactive troubleshooting, freeing capacity for skills development.
4. Build a compliance skills matrix — Map your team’s current regulatory knowledge against your actual compliance obligations (GDPR, PCI DSS, HIPAA, and others relevant to your sector) to identify targeted training investments.
5. Coach IT managers to advocate for budget — The finding that fewer than half of IT professionals feel comfortable requesting technology investment from business leaders is a leadership development gap, not just a communication style issue.

About the Source Data

The SolarWinds IT Trends Report 2020 was conducted in December 2019 and is based on responses from 227 technology practitioners, managers and directors from public and private small, mid-size and enterprise businesses in the U.S. and Canada. The DevOps Institute Upskilling 2020: Enterprise DevOps Skills Report drew on approximately 1,300 respondents globally. Both surveys predate the COVID-19 pandemic, and the pressures they document have in many cases intensified since publication.

BigPanda Launches “IT Ops From Home” Program

BigPanda, a provider of an AI-driven IT operations platform, announced a free 90-day access program aimed at IT professionals working remotely during the COVID-19 pandemic. Branded as the IT Ops From Home program, the initiative gave qualifying organizations no-cost access to BigPanda’s platform along with free support and virtual classroom training for the duration of the trial. There was no obligation to renew at the end of the 90-day period.

What the BigPanda Platform Does

BigPanda’s platform is built around automated IT incident management. Its core capability is event correlation: it ingests alerts and events from across an IT environment and uses machine learning to group related signals into actionable incidents, reducing alert noise and accelerating mean time to resolution (MTTR).

According to Mohan Kompella, Vice President of Product Marketing at BigPanda, the platform was designed from the ground up to support remote management of highly distributed computing environments — a capability that became immediately relevant as IT teams were forced to work from home almost overnight.

Key Integrations

The platform ships with built-in integrations for a wide range of enterprise tools and cloud services, including:

• Cloud platforms: Amazon Web Services (AWS), Microsoft Azure
• Monitoring & observability: Amazon CloudWatch, AppDynamics, Splunk
• Collaboration & ticketing: Jira, Slack

These integrations allow operations teams to centralize visibility across disparate tools without rebuilding their existing toolchains.

Why Remote IT Operations Became a Priority

The COVID-19 pandemic exposed a significant gap in many organizations’ IT management strategies: legacy on-premises platforms were not designed to support fully remote IT operations teams managing distributed application workloads. Business continuity plans that assumed partial or temporary remote work suddenly had to cover entire IT departments working from home indefinitely.

Kompella acknowledged that adopting a new platform in the middle of a crisis is not without friction. However, he argued that the alternative — continuing with tools that were never designed for distributed remote operations — was increasingly untenable. The BigPanda platform’s value proposition in this context was its ability to unify management across the silos that make up a modern enterprise IT environment, regardless of where the operators themselves are physically located.

The Broader Shift: Remote IT Ops as the New Normal

Beyond the immediate crisis response, Kompella suggested that managing IT infrastructure remotely could become a permanent operating model for many organizations — much as remote customer service became normalized in previous decades. The potential benefits include reduced overhead for office space and greater flexibility in staffing IT roles.

This shift aligns with broader trends already underway in enterprise IT: accelerating cloud adoption, embracing DevOps practices, and deploying workloads across multi-cloud and hybrid environments. The pandemic effectively compressed timelines that organizations had planned to execute over years into a matter of months.

Implications for IT Operations Modernization

For DevOps and IT operations teams, the practical takeaways from this period are worth internalizing even after the immediate crisis context has faded:

• Tool sprawl creates resilience risk. Organizations with fragmented monitoring and incident management tools struggled disproportionately when operations teams went remote. Consolidating event management reduces operational dependency on physical co-location.
• Cloud-native architecture enables continuity. Workloads deployed in the cloud with well-defined operational runbooks are easier to manage remotely than those tied to on-premises infrastructure requiring physical access.
• AIOps reduces manual toil. Automated event correlation and noise reduction become more valuable — not less — when the team is distributed and informal escalation paths (tapping someone on the shoulder) are unavailable.
• Business continuity planning must account for fully remote ops. Plans that assumed a skeleton crew could be on-site need to be revisited for scenarios where no one is on-site at all.

Context and Availability

This program was announced in April 2020 as a direct response to the COVID-19 pandemic. The 90-day free access offer was time-limited and tied to the pandemic response context. Readers interested in BigPanda’s current pricing, trial options, or product capabilities should consult BigPanda’s official website for up-to-date information, as offering details will have changed since the original announcement.

Overview

Even against the backdrop of the COVID-19 pandemic in early 2020, cloud computing providers continued to push forward with strategic expansions and leadership changes that directly affected their channel ecosystems. Three announcements stood out: Rackspace’s deepened investment in the Middle East and Africa, IBM’s appointment of a new cloud leader under incoming CEO Arvind Krishna, and Zenoss’s launch of a dedicated EMEA cloud zone. Here’s what each development means for partners and customers.

Rackspace Expands EMEA Presence to Address Cloud Skills Gap in the Middle East

Rackspace — a managed cloud and hosting provider that works across AWS, Google Cloud Platform, and Microsoft Azure — announced a multimillion-dollar investment to deepen its footprint in the Middle East and Africa region. The move was designed to open new opportunities for channel partners, including distributors, master agents, systems integrators, and value-added resellers (VARs) operating in the region.

Lisa McLin, Vice President of Alliances and Channel Chief at Rackspace, told Channel Futures that the expanded investment aimed to bring “additional local resources to help address the dearth of cloud skills in the Middle East.” Rackspace manages private and public cloud deployments on behalf of end users, providing an unbiased, multi-cloud approach that suits the complexity of enterprise environments in the region.

George Pawlyszyn was named General Manager for Rackspace’s Middle East and Africa strategy. According to McLin, Pawlyszyn is focused on “providing trusted and unbiased expertise to migrate customers to the cloud securely, and enhance existing customers’ environments whilst working closely with partners.”

At the time of the announcement, Rackspace already served more than 500 customers in the Middle East. To support growth, the company launched a new hub in Dubai, housing salespeople, professional services consultants, and marketing personnel.

“The cloud market is growing significantly in the region and customers are increasingly requiring unbiased experts to support them on their cloud journey.”

Martin Blackburn, Managing Director, EMEA, Rackspace

For channel partners, this kind of regional investment typically translates to more localized sales support, co-selling opportunities, and access to Rackspace’s multi-cloud managed services portfolio closer to end customers.

IBM Appoints Howard Boville as Senior Vice President of IBM Cloud

In a significant leadership move, newly appointed IBM CEO Arvind Krishna — who himself previously led IBM’s cloud computing division before replacing Virginia Rometty — named Howard Boville as Senior Vice President of IBM Cloud, effective May 2020.

Boville joined IBM from Bank of America, where he served as Chief Technology Officer. Notably, the two organizations had already been collaborating to build cloud platforms tailored for the financial services sector, making Boville a familiar face in the IBM ecosystem.

In his new role, Boville was positioned to lead IBM’s cloud strategy with a specific emphasis on hybrid cloud — a priority that IBM has consistently placed at the center of its competitive positioning against hyperscalers such as AWS, Google Cloud, and Microsoft Azure. He also assumed oversight of IBM’s global cloud infrastructure, which at the time included:

• More than 60 cloud data centers
• Operations spanning 19 countries
• 18 availability zones across six geographic regions

The appointment signaled IBM’s intent to accelerate its hybrid cloud narrative — an approach underpinned by the 2019 acquisition of Red Hat — and to compete more aggressively in enterprise cloud deals where workload portability and data sovereignty are critical concerns.

Zenoss Launches Dedicated EMEA Cloud Zone in Frankfurt

Zenoss, an Austin, Texas-based company that develops monitoring analytics software for cloud, hybrid, and on-premises IT environments, announced the availability of a dedicated EMEA cloud zone for its Zenoss Cloud SaaS platform. The zone is hosted in Frankfurt, Germany.

The Frankfurt location was selected for its strong data protection credentials. Specifically, Zenoss cited compliance with the German Bundesdatenschutzgesetz (BDSG) — Germany’s federal data protection act — as a key factor, alongside alignment with GDPR requirements affecting European customers.

“Zenoss Cloud has taken off domestically, and we’ve been experiencing increased demand for separate instances in other regions. We’re dedicated to serving our customers around the world, and this new zone will provide full-stack monitoring and AIOps with the highest levels of data protection.”

Greg Stock, Chairman and CEO, Zenoss

With this launch, all U.S.-based Zenoss Cloud services became available to EMEA customers from a regionalized, dedicated instance — an important distinction for organizations subject to data residency requirements.

Zenoss Cloud is a SaaS-based platform that provides full-stack monitoring and AIOps capabilities, helping IT teams maintain performance visibility across complex, hybrid environments. The company’s strategic alliance partners at the time included AWS, Accenture, Cisco, and NTT Data. It also offered its software through marketplace partnerships on AWS, CenturyLink, Cisco, Rackspace, and ServiceNow.

Zenoss indicated that additional regional zones were planned following the EMEA launch. According to the company, both partners and customers had already begun using the Frankfurt-based EMEA zone at the time of the announcement.

Key Takeaways for Channel Partners

• Rackspace’s EMEA expansion creates new co-sell and referral opportunities for VARs, MSPs, and systems integrators in the Middle East, backed by a formal hub in Dubai and named regional leadership.
• IBM’s hybrid cloud focus under Boville reinforces the company’s enterprise positioning — partners building IBM-aligned hybrid cloud practices can expect continued investment and messaging support from the vendor.
• Zenoss’s EMEA zone removes a key barrier for European customers concerned about data residency, making the platform easier for EMEA-based partners to position and sell.

All three announcements reflect a broader trend: cloud providers are doubling down on regional infrastructure, local expertise, and partner ecosystems as enterprise adoption of multi-cloud and hybrid environments accelerates globally.

The Core Tension in Digital Transformation

Digital transformation promises competitive advantage, but it routinely fails to deliver lasting value. The reason is a structural tension that every engineering organisation faces: systems must remain stable, secure, and predictable while development and operations teams are simultaneously expected to ship new features rapidly.

This pull between reliability and velocity shapes almost every architectural and tooling decision a team makes. Should you extend existing infrastructure to support new workloads, or redesign your delivery pipeline from scratch? The answer organisations are increasingly landing on is a cloud-native development strategy—and for good reason.

What Cloud-Native Actually Means

“Cloud-native” is used loosely, but at its most precise it describes an architectural approach with these defining characteristics:

• Microservices architecture: Applications are decomposed into small, independently deployable services, each owning a single business capability.
• Linux containers: Services are packaged and run in containers, making them portable across environments and easy to scale.
• API-first communication: Services interact through well-defined APIs, keeping them loosely coupled and replaceable without cascading changes.
• DevOps and CI/CD: Small, cross-functional teams own the full lifecycle of a service—from development through deployment and on-call operations—using continuous integration and continuous delivery pipelines.

When these elements work together, individual services can be built quickly, deployed automatically, and updated on independent release cadences. A change to the payment service no longer requires a full-application release freeze.

The Role of Modern Middleware

Traditional middleware—the software layer that connects applications, data sources, and users—was designed for monolithic, long-running server deployments. It does not translate cleanly to a containerised, ephemeral environment.

Modern middleware must be refactored to align with cloud-native assumptions: it needs to be lightweight, start quickly, expose APIs, and integrate naturally with container orchestration platforms such as Kubernetes. One example is Quarkus, a Kubernetes-native Java framework designed to reduce JVM startup time and memory footprint, making Java workloads viable in serverless and container-dense environments. Quarkus is positioned to bring existing Java skill sets into cloud-native delivery without requiring a complete rewrite in a new language.

Building a Modern Infrastructure Foundation

Container platforms do more than package code—they shift orchestration responsibility away from application servers and into the infrastructure layer. This separation means application teams can focus on business logic while the platform handles scheduling, scaling, self-healing, and service discovery.

Two infrastructure principles that have become standard practice in cloud-native environments:

• Fail-fast design: Container infrastructure makes it straightforward to spin replacement instances up and down, so rather than trying to prevent all failures, teams design systems that recover from them automatically and quickly.
• Chaos engineering: Popularised by Netflix, chaos engineering involves deliberately injecting failures into production systems to validate that resilience mechanisms actually work under real conditions—not just in theory.

Organisations with workloads still locked in legacy environments face a harder path. Those systems accumulate technical debt, are expensive to operate, and resist the automation that cloud-native delivery requires. Migrating or modernising those workloads—even incrementally—is a prerequisite for simplifying the broader IT environment.

Why CI/CD Is Central to the Cloud-Native Value Proposition

Continuous integration and continuous delivery are not optional extras in a cloud-native strategy—they are the mechanism by which microservices provide value. Without CI/CD, independently deployable services create operational complexity rather than reducing it.

Key benefits CI/CD brings to a cloud-native environment:

• Automated quality gates: Tests run at every stage of the pipeline, catching defects early before they compound.
• Fast feedback loops: Developers learn within minutes whether a change breaks existing behaviour, rather than discovering problems during a quarterly release cycle.
• Reduced deployment risk: Frequent, small deployments are statistically less risky than infrequent, large ones. Problems are easier to isolate and roll back.
• Continuous delivery of value: Business stakeholders see working features sooner, and can redirect priorities based on real usage rather than roadmap assumptions.

The Observability and Resilience Stack

A distributed microservices architecture is harder to reason about than a monolith. You trade a single failure point for many smaller ones, and debugging a latency issue that spans five services requires different tooling than reading a single application log.

Cloud-native operations depends on three pillars:

• Observability: Metrics, distributed tracing, and structured logs must be built into every service from the start—not bolted on after incidents reveal blind spots.
• Manageability: Services need health endpoints, graceful shutdown behaviour, and configuration externalised from code so that operators can manage them without redeployment.
• Resilience: Patterns such as circuit breakers, retries with back-off, and bulkheads prevent a failing downstream service from cascading failures through the entire application graph.

Culture and People: The Non-Technical Requirements

Technology alone does not deliver cloud-native outcomes. Organisations that invest in Kubernetes clusters and CI/CD pipelines while leaving team structures, incentives, and communication patterns unchanged typically see limited improvement.

A genuine DevOps culture requires:

• Shared ownership: Development teams take responsibility for the operational behaviour of their services, not just their source code.
• Psychological safety: Engineers need to be able to surface problems, propose experiments, and report failures without fear of blame. This is a prerequisite for the blameless post-mortems that drive real learning.
• Continuous skill development: Cloud-native technology evolves quickly. Organisations must invest in upskilling existing staff rather than assuming that hiring alone closes capability gaps.
• Cross-functional collaboration: Silos between development, security, and operations are the single biggest process bottleneck in most large organisations. Breaking them requires deliberate structural change, not just tooling.

A Practical Path Forward

Cloud-native transformation is rarely a single, big-bang migration. A more durable approach is incremental:

1. Assess your current portfolio. Identify which workloads are candidates for containerisation versus which need re-architecture versus which should simply be retired.
2. Establish a container platform. Kubernetes (or a managed distribution of it) is the de facto standard. Get the platform running and stable before migrating workloads at scale.
3. Build your CI/CD foundation. Automate build, test, and deployment pipelines for new services before tackling legacy migration.
4. Start with a bounded, low-risk service. Pick a service that is relatively self-contained and non-critical for your first cloud-native deployment. Use it to validate your platform and practices before scaling.
5. Invest in observability from day one. Instrument services with metrics and distributed tracing before you need them in an incident—not after.
6. Iterate on culture in parallel. Team structures, on-call processes, and engineering practices need to evolve alongside the technology.

The Long-Term Competitive Case

Cloud-native architecture is not primarily about cost reduction—though operational efficiency is a real benefit. The deeper value is the organisational ability to respond to market change faster than competitors whose monolithic systems require multi-month release cycles.

When application delivery is measured in hours rather than months, businesses can run more experiments, respond to user feedback quickly, and reduce the cost of being wrong. That compounding advantage is what makes cloud-native strategy genuinely future-proof—not any specific technology version or vendor platform.

The organisations that extract the most value from cloud-native technology are those that treat it as a continuous programme of technical and cultural improvement, rather than a project with a defined end date.

Kubernetes Brings More Data to the Table — but That Complicates Observability

A key driver of this complexity is the rapid adoption of Kubernetes. Surveys conducted around 2019–2020 pointed to Kubernetes becoming the dominant container orchestration platform, with the majority of enterprises either already running it in production or planning to do so within the year. The appeal is straightforward: Kubernetes gives development teams the flexibility to release services, features, and applications faster than traditional deployment models allow.

Faster delivery does not automatically mean higher-quality delivery, however. To capture the full value of Kubernetes, teams also need to adopt cloud-native architectural patterns, embed quality gates into the delivery pipeline, use modern deployment strategies such as canary releases or feature toggles, and maintain short, meaningful feedback loops from production back to engineering.

Kubernetes itself offers solid built-in visibility at the node and pod level, including health-check mechanisms and — through service meshes — live dependency data between services. In theory, this wealth of data should give organizations the insight they need to understand whether they are releasing better value faster. In practice, more data does not automatically produce more visibility. Large Kubernetes environments can contain literally billions of interdependencies spanning applications, containers, microservices, and cloud providers. No human team can track that volume of relationships manually, and relying on traditional, threshold-based monitoring approaches fails to capture the signal buried in the noise.

Full-Stack Observability Requires Deterministic AI

Getting full value from Kubernetes means achieving observability across the entire technology stack — not just individual nodes or pods, but the billions of interdependencies between every component. That scope makes AI assistance not a luxury but a practical necessity. The specific capability needed is a deterministic AI engine: one that applies well-defined algorithms to detect anomalies and trace their root causes through a continuously updated dependency map of the environment.

When a deterministic AI engine ingests Kubernetes events — state changes, workload shifts, resource allocation updates — it builds and maintains a real-time topology of the stack. This enables capabilities that traditional metrics and dashboards cannot match:

• Automatic instrumentation and mapping of containers as they are created, scaled, or destroyed.
• Dynamic resource management that reflects the current state of the cluster rather than static thresholds.
• Deep runtime inspection that correlates container-level events with broader service behavior.
• Real-time root-cause identification for degradations in service availability, performance, or user experience — surfaced in seconds rather than hours.

Because the AI resolves issues at the source before they propagate to end users, and feeds intelligence back into DevOps release pipelines, the result is a measurable acceleration of both deployment speed and quality. Teams previously bogged down in manual incident triage are freed to focus on work that creates business value.

AI-Powered Observability Depends on Quality Data and Consistent Monitoring

A deterministic AI engine is only as reliable as the data it ingests. If different teams inside a large organization bring their own preferred monitoring tools into a Kubernetes cluster, the data fed to the AI becomes inconsistent. Gaps, format mismatches, and coverage blind spots degrade the accuracy of dependency mapping and anomaly detection.

The practical solution is to establish a single, standardized monitoring layer that functions as a self-service capability across all Kubernetes clusters. This unified layer should:

• Support the full diversity of technologies introduced into the stack (multiple languages, frameworks, and cloud providers).
• Produce a consistent baseline of system and event data that developers, platform engineers, and the AI engine all draw from.
• Scale automatically as clusters grow, without requiring manual instrumentation for each new workload.

Consistent, high-quality input data makes the AI engine progressively smarter. A smarter engine delivers more precise observability, which in turn supports faster and more confident deployments.

Building a Positive Feedback Loop Between Kubernetes, Deterministic AI, and Observability

Kubernetes and deterministic AI are a natural pairing. Kubernetes provides the platform for orchestrating and instrumenting containerized workloads; the deterministic AI provides the intelligence layer for mapping interdependencies, detecting degradations, and resolving issues before they reach users. The relationship is self-reinforcing:

1. Kubernetes generates rich operational data as workloads run and evolve.
2. The deterministic AI ingests that data, builds a continuous dependency topology, and identifies anomalies with root-cause context.
3. Insights from the AI feed back into release pipelines and cluster configuration, improving the quality of the next deployment cycle.
4. Better deployments produce cleaner operational data, which further sharpens the AI’s models.

This feedback loop replaces the fragility of manual monitoring with a system that becomes more capable as the environment grows — exactly the opposite of the traditional model, where complexity outpaces human capacity. The end result is faster deployments, higher reliability, better user experiences, and more time for engineering teams to work on problems that matter.

Key Takeaways

• Kubernetes adoption has made container orchestration the default model for enterprise application delivery, but it also introduces billions of runtime interdependencies that exceed manual monitoring capacity.
• Deterministic AI — applying specific algorithms to a continuously updated dependency map — is the practical mechanism for achieving full-stack observability at Kubernetes scale.
• The accuracy and value of AI-powered observability is directly proportional to the consistency and quality of the monitoring data it receives; fragmented tooling undermines the AI’s effectiveness.
• A unified monitoring layer feeding a deterministic AI engine creates a self-reinforcing loop: better data produces smarter AI, which produces better observability, which supports better deployments.

Why AWS Certifications Still Matter for IT Careers

AWS consistently ranks as the largest cloud provider by market share. Organizations across finance, healthcare, media, retail, and the public sector rely on AWS infrastructure, which means hiring managers across those verticals actively look for AWS expertise when filling data engineering, DevOps, and solutions architect roles. An AWS specialty certification serves as an objective proof of that expertise during a competitive hiring process.

While general cloud literacy is increasingly common, specialty certifications target practitioners who can design, build, and secure complex workloads on a specific domain—such as big data and analytics—rather than simply describing what a service does.

What the AWS Data Analytics Specialty Exam Covers

AWS retired the original “Big Data – Specialty” exam and replaced it with the AWS Certified Data Analytics – Specialty (DAS-C01). The updated exam tests five primary domains:

• Collection – Kinesis Data Streams, Kinesis Data Firehose, AWS Glue, Database Migration Service (DMS), and choosing the right ingestion pattern for throughput and latency requirements.
• Storage and Data Management – S3 storage classes, data lifecycle policies, DynamoDB, and Lake Formation for organizing data lakes.
• Processing – Amazon EMR (Elastic MapReduce), AWS Glue ETL jobs, Lambda for event-driven transformation, and Spark/Hive on EMR.
• Analysis and Visualization – Amazon Athena for serverless SQL querying, Amazon Redshift for data warehousing, QuickSight for BI dashboards, and OpenSearch (formerly Elasticsearch Service).
• Security – Encryption at rest and in transit, IAM policies, VPC configurations, Lake Formation permissions, and CloudTrail auditing across the data pipeline.

Key AWS Services to Master Before the Exam

Amazon EMR (Elastic MapReduce)

EMR is AWS’s managed cluster platform for running big data frameworks such as Apache Spark, Hadoop, Hive, and Presto. Exam questions often focus on cluster sizing, instance fleet versus instance group configurations, cost optimization with Spot Instances, and choosing between persistent and transient clusters depending on the workload.

AWS Glue

Glue is a serverless ETL service that also provides a central data catalog. Understanding the crawler-to-catalog-to-job pipeline, DynamicFrame vs. DataFrame in Glue ETL scripts, job bookmarks for incremental processing, and Glue DataBrew for no-code transformations is essential for the exam and for real-world data engineering work.

AWS Lambda

Lambda appears in data pipelines as the event-driven glue between services—triggering transformations when a file lands in S3, reacting to Kinesis stream records, or orchestrating lightweight processing steps. Know its concurrency limits, execution timeout constraints, and integration with Step Functions for complex workflows.

Amazon Kinesis

Real-time data ingestion is a core exam topic. Understand the difference between Kinesis Data Streams (configurable retention, custom consumers, replay capability) and Kinesis Data Firehose (managed delivery to S3, Redshift, or OpenSearch with built-in transformation via Lambda).

Amazon Redshift

Redshift is AWS’s columnar data warehouse, and it carries significant weight in the exam. Focus on distribution styles (KEY, ALL, EVEN, AUTO), sort keys, Redshift Spectrum for querying S3 without loading data, COPY command tuning, and Workload Management (WLM) configuration.

Prerequisites and Recommended Experience

AWS recommends at least five years of experience with data analytics and two or more years of hands-on AWS experience before attempting the Data Analytics – Specialty exam. Candidates who have already passed the AWS Certified Cloud Practitioner or AWS Certified Solutions Architect – Associate exams will find the foundational concepts familiar and can focus their study time on the analytics-specific services.

Practical lab work matters more than video hours alone. Set up an end-to-end pipeline—ingest records with Kinesis, process them with Glue or Lambda, store the output in S3 and Redshift, then query with Athena—before sitting the exam.

How to Structure Your Study Plan

1. Review the official exam guide. AWS publishes a detailed exam guide listing every domain and the percentage of questions drawn from each. Use it to weight your study time accordingly.
2. Complete a structured video course. Multi-hour prep courses (typically 15–20 hours of content) provide systematic coverage of the exam domains. Look for courses updated to the DAS-C01 blueprint.
3. Work through hands-on labs. AWS offers a free tier with access to many of the relevant services. AWS Skill Builder also provides guided labs and practice environments.
4. Practice with timed exams. Official AWS practice exams and third-party question banks help you identify weak areas and build test-taking stamina. The exam is 65 questions in 180 minutes.
5. Review AWS whitepapers and FAQs. The AWS Big Data Whitepaper, the Streaming Data Solutions on AWS whitepaper, and the FAQs for Redshift, EMR, and Kinesis are common sources for nuanced exam questions.

Career Outlook for AWS-Certified Data Specialists

AWS-certified professionals are in demand across a wide range of industries. Roles that commonly list AWS Data Analytics or Big Data specialty credentials as a requirement or preference include:

• Data Engineer
• Cloud Data Architect
• Analytics Solutions Architect
• BI Platform Engineer
• DataOps / MLOps Engineer

Compensation for certified AWS data engineers varies significantly by geography, industry, and seniority. Researching current salary ranges on sites such as Levels.fyi, LinkedIn Salary, or the Stack Overflow Developer Survey will give you the most accurate and up-to-date figures for your specific market.

Bottom Line

The AWS Certified Data Analytics – Specialty exam is a rigorous credential that rewards genuine platform experience. It is not a shortcut to a high-paying role—but for engineers who already work with data pipelines and want to formalize and validate their AWS expertise, it is a well-recognized signal that can open doors to more senior positions. Pair structured video instruction with consistent hands-on lab practice, and use the official exam guide to keep your preparation focused.

Note: AWS periodically updates its certification catalog and exam blueprints. Always check the AWS Certification page for the current exam version and guide before purchasing study materials.

A Company Shaped by Acquisitions

For much of the Linux era, SUSE occupied a prominent position as one of the two dominant forces driving enterprise Linux adoption, sitting alongside Red Hat. What followed, however, was one of the more convoluted ownership histories in enterprise IT.

After relocating its headquarters from Germany to Oakland, California, SUSE was sold to Novell. Novell was subsequently acquired by Attachmate. Three years later, Attachmate was acquired by Micro Focus. Under Micro Focus, SUSE absorbed OpenStack distributions and the Cloud Foundry platform-as-a-service (PaaS) assets from Hewlett Packard Enterprise (HPE).

In 2018, SUSE was spun out of Micro Focus, returning to independent status and relocating its headquarters back to Germany. In 2019, Melissa Di Donato — previously Chief Operating Officer at SAP — was appointed CEO. Shortly after, SUSE announced it would discontinue its OpenStack distribution to concentrate resources on cloud-native technologies, specifically the opportunities emerging from the rapid adoption of Kubernetes.

The Cloud-Native Pivot: Kubernetes at the Core

SUSE’s strategic repositioning centered on Kubernetes deployed on its own Linux operating system. This combination served as the foundation for two key offerings:

• Container-as-a-Service (CaaS): The SUSE CaaS Platform, built directly on Kubernetes and SUSE Linux.
• Platform-as-a-Service (PaaS): A Cloud Foundry deployment running on top of Kubernetes, giving development teams a higher-level application platform.

Brent Schroeder, Global CTO at SUSE, described the strategy as a response to the increasing complexity and heterogeneity of modern IT environments. As more workloads migrate to public cloud infrastructure, organisations need a consistent substrate — one that works equally well on virtual machines, bare-metal servers, and across cloud providers.

“Virtual machines, bare-metal and the cloud will be a huge catalyst.”

Brent Schroeder, Global CTO, SUSE

Schroeder’s point reflects a broader industry reality: Kubernetes has become the de facto orchestration layer for containerised workloads, and vendors that build platforms on top of it inherit the flexibility and portability Kubernetes provides. Whether an organisation deploys on a private data centre or a hyperscaler, a Kubernetes-based foundation allows them to standardise tooling, processes, and automation.

DevOps as a Strategic Driver

Schroeder identified DevOps adoption as a primary catalyst for the transitions SUSE was positioning itself to support. Enterprises are not only asking how to build and ship software faster — they are equally concerned with how to secure and govern those pipelines at scale.

A concrete example of this focus was visible in the SUSE CaaS Platform 4 release, which added support for Cilium, an open-source project that uses eBPF (extended Berkeley Packet Filter) to enforce network security policies between containerised application services. Cilium’s inclusion reflected growing demand from enterprise teams for fine-grained, identity-aware network security within Kubernetes clusters — a requirement that mature DevSecOps practices surface quickly.

Key DevOps capabilities that SUSE’s platform strategy addressed at that time included:

• Faster application delivery: Kubernetes-native CI/CD pipelines enabled by the CaaS platform.
• Hybrid cloud portability: Consistent runtime environments across on-premises and public cloud deployments.
• Container network security: Cilium integration for policy enforcement between microservices.
• Developer self-service: PaaS capabilities via Cloud Foundry on Kubernetes, abstracting infrastructure complexity from application teams.

The Competitive Landscape: Red Hat and the CaaS/PaaS Convergence

SUSE was not alone in this strategic direction. Red Hat had been the first major vendor to build a PaaS environment on top of Kubernetes — OpenShift — and had been actively working to eliminate the distinction between CaaS and PaaS through a unified platform experience. IBM’s 2019 acquisition of Red Hat added significant resources and an enterprise sales infrastructure to the OpenShift effort, anchoring IBM’s hybrid cloud strategy on the platform.

The IBM-Red Hat acquisition, however, created a dynamic that potentially benefited SUSE. Organisations that had evaluated or adopted Red Hat OpenShift now found themselves working with an IBM-owned entity — a shift that some enterprise buyers viewed with uncertainty. For SUSE, an independent vendor with a focused Kubernetes portfolio, this created a natural opening to position itself as an alternative without the enterprise-conglomerate overhead.

What This Means for DevOps Teams

SUSE’s trajectory in 2019 illustrated a pattern that recurs throughout enterprise software: vendor consolidation in the infrastructure layer creates both risk and opportunity for engineering teams. Practically, DevOps practitioners evaluating container platforms should account for:

• Vendor independence and long-term viability: A platform tied to a single corporate parent carries different risks than one backed by an independent or widely distributed ecosystem.
• Upstream Kubernetes alignment: Platforms built closer to upstream Kubernetes reduce lock-in and ease migration between providers.
• Security posture: Built-in network policy enforcement (such as Cilium) should be a baseline expectation, not an afterthought.
• Hybrid and multi-cloud flexibility: The ability to run consistently across bare-metal, VM, and public cloud targets is increasingly a hard requirement for platform selection.

Looking Ahead

As of the time of this reporting (late 2019), SUSE’s ultimate path — whether an IPO, further acquisition, or continued independent operation — remained open. What was clear was the company’s intent to compete seriously in the cloud-native space by anchoring its portfolio on Linux, Kubernetes, and container platforms designed for enterprise DevOps workflows.

For DevOps and platform engineering teams, the broader lesson from SUSE’s journey is that the enterprise infrastructure landscape is rarely static. Evaluating platforms requires not just assessing today’s capabilities, but also understanding the corporate and ecosystem context that will determine whether a platform is maintained, evolved, or eventually discontinued.

Over the past 10 years, the idea of combining development and production within IT operations – known as DevOps – has spread throughout the world of IT.

A report by Markets and Markets estimates the global DevOps market growth at up to $10.31 Billion by 2023. The demand for DevOps will be driven by several factors such as lower costs, flexibility, agility, and fast application delivery. Therefore, this opens up significant career opportunities for DevOps engineers in India and worldwide.

The benefits of complementing DevOps with the ITIL framework should not be underestimated: balancing the needs of fast turnaround in software delivery offered by DevOps with the stability in managing IT and digitally-enabled services provided by ITIL.

Abhishek Maitrey, Project Manager at the British Council Shared Service Centre in Noida, India, said: “DevOps as a cross-functional approach fits each part of software development including development build management, testing pre-deployment, release management, configuration management and monitoring. It values collaborative work and increases the frequency and quality of a complete software product.”

The fast delivery of quality products through DevOps practices is, according to Maitrey, making a huge difference to the Indian IT market and playing an important role in the stable growth of industry and business.

Heightened interest in the DevOps movement has led to events such as the DevOps India Summit which – in 2018 – was reputedly the first-ever, large scale DevOps event in the country. Marc Hornbeek – Principal Consultant DevOps with Trace3 and DevOps – speaking in The Economic Times in August this year, ahead of the DevOps India Summit 2019, said: “I see India as the capital for DevOps workforce for the world. DevOps takes a lot of effort, focus for execution and maintenance, but the benefits of DevOps are worth it. For an effective DevOps it is important to have a respectful collaboration of People, Process and Tools.”

He added that DevOps in enterprise is still in its early stages with few “unicorns” and suggested that, in the next five years, there will be an ongoing need for DevOps, with early signs of smarter tools being used to integrate its application with artificial intelligence.

The potential identified for IT in India is clear, while – at the same time – it should be measured with some caution and a recognition that other frameworks, such as ITIL, are there to apply sound governance.

DevOps in MNCs and start-ups

A report from Indian employment technology company, Belong, noted that the Indian DevOps skills base is “concentrated in Bangalore, National Capital Region, Pune, and Hyderabad, comprising 63 percent of the overall talent pool”.

Manu Siddharth Jha writing on the Great Learning website also highlighted India as a destination for global IT multi-national companies (MNCs), with the growth in development centres leading to a boom in DevOps jobs to support technologies such as cloud and artificial intelligence.

Equally, the Indian technology start-up market is notable for its “fast-paced work environment” in which “getting their products out on time will be crucial for their business”, therefore, increasing the demand for DevOps executives to enhance the product delivery process.

Speed, while clearly critical, should not obscure the need in all organizations to maintain a stable, business as usual IT environment.

DevOps and ITIL 4

In a world where a variety of different methods and frameworks are used to manage digital product development and service management, how do they work alongside DevOps?

ITIL, the world’s most widely-adopted framework for IT service management, has now evolved into ITIL 4. Its up-to-date structure and content now make it more applicable to working with and integrating modern and emerging practices such as DevOps and Agile and supporting digital transformation.

Organizations are now recognizing the need to incorporate a range of different approaches to ensure digital transformation and service management success – and ITIL 4 supports those concepts. For example, it provides software development teams with additional capability and terminology to work collaboratively with service teams.

As DevOps harnesses Agile software development methods along with service management techniques, extra value is possible when combining it with ITIL 4. This includes streamlining practices such as change enablement, managing unplanned incidents and investigating how failure has happened.

With ITIL 4’s focus on the co-creation of value through managing digitally-enabled services, the involvement of DevOps supports areas in software product development such as operability, reliability, and maintainability.

And ITIL 4’s Guiding Principles, including Focus on Value and Progress Iteratively with Feedback, blend well with DevOps’ approaches of continual experimentation, taking risks and learning from success and failure.

Abhishek Maitrey – who is also ITIL-certified – adds: “Many organizations are already using DevOps with ITIL across the globe, including India, and we also in the planning phase of managing our services through DevOps.”

India – embracing DevOps

Speaking at the DevOps India summit this year, Marc Hornbeek reportedly said that the Indian IT community had really embraced DevOps and particularly the approach of outsourcing the discipline wholly to work on complete projects rather than segments.

According to Entrepreneur India, he added: “It [DevOps] is a great platform for people to connect and meet people who are willing to learn and ask many important relevant questions. In the near future, DevOps will go through transformation in terms of patterns of practice which will be recognized as high performance that has transformed business.”

And while DevOps is helping Indian IT professionals add extra skills to their core competencies, Maitrey acknowledges that the approach is essentially a culture that organizations have to adapt.

Adopting DevOps and ITIL together means software development and service management progress holistically, and benefit is realized across the entire IT function to achieve greater efficiencies and ensure an organization continues to co-create high value with its stakeholders.

Key Findings: Cloud-Native Security Is a Growing Concern

At the Cloud Native Security Summit, Enterprise Strategy Group (ESG) released the results of a survey of 600 senior IT leaders examining how organizations are approaching security in cloud-native environments. The headline finding: DevSecOps automation has become the top cloud security priority as teams struggle to keep pace with the complexity of modern application delivery.

The numbers paint a clear picture of an industry under pressure:

• 43% of respondents identified maintaining consistency across disparate infrastructures as their biggest challenge with cloud-native applications — and the same proportion named DevSecOps automation as their highest cloud security priority.
• 90% expressed concern about lacking visibility into misconfigured cloud services, server workloads, network security, or privileged accounts.
• 83% said they were worried about insider misuse of privileged accounts.
• 66% reported that IT is more complex than it was two years ago, with 35% citing the management of multiple cybersecurity controls as a major contributor to that complexity.

Why DevSecOps Is Rising on the Cloud-Native Agenda

Doug Cahill, senior analyst and group director at ESG, noted that as organizations mature their DevOps practices for building and deploying cloud-native applications, security is increasingly being incorporated as part of the quality assurance process — rather than retrofitted after the fact. The industry is moving from “bolt-on” security toward building cybersecurity controls into software from the ground up.

Capsule8 CEO John Viega framed much of this shift as necessity-driven. In traditional on-premises environments, security teams could rely on physical or virtual appliances to protect a controlled perimeter. That approach breaks down in the cloud, where organizations operate under a shared responsibility model, often spanning multiple cloud service providers, and where they no longer control the underlying infrastructure.

Viega also highlighted a practical problem that is easy to underestimate: in many large organizations, hundreds of cloud accounts have been created independently by individuals across different business units — each with widely varying levels of security knowledge and practice. This ad-hoc sprawl creates significant exposure that traditional security tooling is poorly equipped to address.

The Speed Problem: Security Teams Cannot Keep Pace

Obsidian Security CTO Ben Johnson pointed to velocity as a core challenge. The rate at which cloud-native applications are deployed and updated is outrunning the capacity of dedicated security teams to review and respond. Many organizations, Johnson suggested, are still underestimating the full scope of the cloud-native security challenge they face.

This velocity gap is precisely why DevSecOps — integrating security practices and tooling directly into CI/CD pipelines — is gaining traction. Automated security gates, policy-as-code, container image scanning, and infrastructure-as-code validation are examples of controls that can operate at the speed of modern delivery pipelines without requiring a security review for every deployment.

Shifting Left: Security Expertise Moves to Development Teams

Hala Al-Adwan, VP of Technology at Signal Sciences, observed that while cybersecurity professionals will remain essential as consultants and architects, the day-to-day responsibility for implementing security controls is steadily shifting left — toward the developers and DevOps engineers who build and operate applications.

This shift creates a tooling challenge. Legacy security infrastructure was designed for specialist administrators who could interpret complex dashboards and manage dedicated appliances. DevOps teams need something different: programmable, API-driven security tooling that integrates natively into continuous integration and continuous deployment (CI/CD) workflows. Tools that generate too much noise or require context-switching out of the developer workflow are unlikely to see consistent adoption.

The Cultural and Scaling Challenge

Even with strong organizational intent, DevSecOps adoption faces a fundamental supply-and-demand problem. There are not enough trained cybersecurity professionals to embed one in every development squad or participate in every sprint. The practical goal, therefore, is not to staff every team with a security specialist — it is to encode security knowledge into tooling, automation, and guardrails that development teams can use without deep security expertise.

This is where investments in developer security education, secure-by-default platform configurations, and automated policy enforcement become critical. Security must scale with the organization, not depend on heroic individual effort.

Will Cloud-Native Applications Actually Be More Secure?

In principle, cloud-native architectures — built on containers, microservices, and immutable infrastructure — offer meaningful security advantages over legacy monolithic applications. Smaller blast radius, shorter-lived workloads, and infrastructure-as-code all create opportunities for stronger security posture.

In practice, realizing those advantages depends on teams actually implementing security controls throughout the development lifecycle. The survey findings suggest that the industry recognizes the priority, but the gap between intent and execution remains significant. Whether improved security outcomes will also motivate organizations to accelerate migration away from legacy monolithic applications is an open question.

Key Takeaways for DevOps and Platform Engineering Teams

• Audit cloud account sprawl. Unmanaged or loosely governed cloud accounts are a leading source of misconfiguration risk.
• Invest in visibility tooling. If your team cannot see misconfigured services, privileged account activity, or network anomalies, you cannot respond to them.
• Treat security as a pipeline stage, not a gate. Automated security checks embedded in CI/CD reduce friction and scale naturally with delivery velocity.
• Prioritize programmable, developer-friendly security tools. Legacy appliance-style tooling does not fit modern delivery workflows.
• Plan for knowledge transfer, not just headcount. Security expertise must be encoded into templates, policies, and guardrails — not just held by specialists.

Survey conducted by ESG and commissioned by Capsule8, Obsidian Security, and Signal Sciences. Original coverage via devops.com.

Security Moves from Afterthought to Boardroom Priority

AWS re:Inforce, Amazon Web Services’ inaugural security-focused conference, brought together practitioners, executives, and vendors to discuss the rapidly evolving challenge of securing public and multi-cloud environments. A recurring theme: security is no longer an afterthought bolted on at the end of a product launch cycle — it is now a first-class concern at the C-suite and board level.

Brian Riley, Senior Director of Global Cyber Risk Management at Liberty Mutual, illustrated how dramatically the conversation has shifted. “As soon as security would come up, out came the BlackBerrys,” he recalled of executive presentations from seven or eight years ago. “The last couple of years there’s been a drumbeat in the press of security issues that have affected various organizations, and there’s much greater focus [on security] at the executive management level and at a board level.”

That cultural shift was on full display at re:Inforce. Alongside deep-dive technical sessions on specific AWS security features and services, the conference dedicated significant time to broader organizational topics: breaking down the silos between security professionals and development teams, and embedding security thinking earlier in the software delivery lifecycle.

Security Teams Must Move at Development Speed

Bill Shinn, Senior Principal Engineer in the Office of the CISO at AWS, emphasized that the shift from large capital expenditure data center investments to pay-as-you-go cloud consumption has fundamentally changed the pace at which security teams must operate.

“I think security teams have to go faster. And they have to be the fastest thing in the organization,” Shinn said. He cautioned that organizations carrying over a data center mindset when architecting security in the cloud will be left behind.

Riley echoed this point from an enterprise perspective: “We can actually implement those security controls more effectively if we partner with the teams that are building those pipelines and build those controls through automation, so that we can execute them at a scale that we’re simply not going to be able to [with] staff in a security organization.”

How AWS Builds Security Into Its Own Development Process

Shinn described how AWS approaches security internally across its service teams. Every service undergoes a rigorous application security review before launch, encompassing:

• Threat modeling — service teams closest to the architecture develop their own threat models, with support from the security team where needed.
• Static and dynamic analysis — automated code and runtime analysis as part of the review pipeline.
• Canary deployments — new features are released to a limited subset of users or systems to validate security posture before broad rollout.

“Once you state the security intent of how something should or shouldn’t exist in production, we have a set of canaries around making sure that state doesn’t change,” Shinn explained.

A cultural emphasis on ownership is central to this model. Service teams are expected to understand and own the security of what they build, with the central security team serving as an accelerator rather than a gatekeeper. “The service teams just know culturally that it’s their job. And we’re there to help them do it faster.”

To reduce friction further, AWS runs an affinity program that pairs service teams with consistent security engineers across significant feature changes. This eliminates repeated context-setting while still allowing fresh engineers to be brought in for new perspectives when warranted. Customer satisfaction (CSAT) scores are used to measure whether security engagements were frictionless, cost-effective, and resulted in teams shipping secure code.

Multi-Cloud Environments Introduce Unique Security Complexity

One of the most substantive themes at re:Inforce was the security complexity introduced by multi-cloud architectures. Riley noted that AWS and Microsoft Azure, for example, have made fundamentally different architectural decisions — each logical and well-suited to their respective platforms — but this creates a meaningful challenge for enterprises trying to maintain consistent security postures across providers.

“Being in a multi-cloud environment makes some security processes more challenging because Amazon has made very different architectural decisions than Microsoft made, for extremely good reasons that make great sense with their platforms,” Riley said. “That means from an enterprise perspective that we really need to take each cloud provider on their own terms.”

He also raised a practical concern about vendor tools that promise security consistency across cloud providers: in practice, pursuing that consistency can mean foregoing the native capabilities that make each platform distinctive and powerful.

The key questions organizations must answer include:

• Where should the enterprise invest in platform-specific security expertise versus cross-cloud tooling?
• How do you ensure the right people are deployed in environments they deeply understand?
• How do you govern policy consistently while still leveraging platform-native capabilities?

The Cybersecurity Talent Gap Remains a Critical Challenge

Beyond technology, both Riley and Shinn pointed to the talent shortage as one of the most pressing issues facing security organizations today.

“There’s demand for talent in this space that far outstrips supply,” Riley said. Liberty Mutual’s approach has been to recruit globally, accepting that the competition for skilled security professionals requires casting a wide net. Organizations that can leverage global scale in hiring have a meaningful structural advantage in building out security teams.

The talent gap also reinforces the case for automation: when qualified security engineers are scarce, embedding security controls into automated pipelines becomes a force multiplier for whatever security staff an organization does have.

New Services Launched at re:Inforce: Control Tower and Security Hub

AWS re:Inforce 2019 served as the launch platform for two new AWS services aimed at helping customers manage governance and security at scale.

AWS Control Tower

AWS Control Tower provides customers with a pre-configured multi-account AWS environment and a pre-packaged set of guardrails to support ongoing governance and compliance. It is designed to answer demand for out-of-the-box governance without requiring organizations to build their own frameworks from scratch.

“Control Tower is an answer to what customers are really asking for in terms of governance and guardrails out of the box,” Shinn said. “Control Tower is launching with very prescriptive guardrails. And we also still have the Landing Zone solution available for customers that want a higher degree of configuration or customization.”

In practice, Control Tower is suited to organizations that want a fast, opinionated path to a governed multi-account environment, while Landing Zone caters to teams that need deeper customization.

AWS Security Hub

AWS Security Hub provides a centralized view of security and compliance findings across an AWS environment. It aggregates findings from native AWS services and integrated third-party partner solutions, normalizes them, and surfaces high-signal, actionable findings in a single console.

“Security Hub is really about meeting customers where they’re at in terms of integrations and event flows. Taking findings and high signal findings and making them actionable is something that Security Hub has done well within the public preview,” Shinn said.

Security Hub can integrate with Amazon CloudWatch and AWS Lambda, enabling automated remediation workflows triggered by specific findings — a key capability for security teams that need to operate at cloud scale without proportional headcount increases.

Key Takeaways for Security and DevOps Teams

• Shift security left: Embedding security reviews, threat modeling, and automated analysis into CI/CD pipelines is more effective and scalable than end-of-cycle security gates.
• Treat security as an enabler: Security teams that act as partners to development teams — rather than blockers — produce better outcomes and ship secure code faster.
• Respect cloud-native architectures: In multi-cloud environments, platform-native security controls often outperform generic cross-cloud solutions. Invest in cloud-specific expertise.
• Automate to compensate for talent scarcity: Given the persistent talent gap, automation and policy-as-code approaches are essential for scaling security programs.
• Use centralized visibility tools: Services like AWS Security Hub reduce the overhead of correlating findings from disparate tools and enable faster, automated response.