Cloud security, also known as cloud computing security, is a collection of security measures designed to protect cloud-based infrastructure, applications, and data. These measures ensure authentication of users and devices, access control for data and resources, and protection of data privacy. They also support data regulatory compliance.

But as companies move more data and applications to the cloud, IT professionals remain concerned about security, governance, and compliance issues when their content is stored in the cloud. They worry that highly sensitive business information and intellectual property may be exposed through accidental leaks or due to increasingly sophisticated cyber threats.

A crucial component of cloud security is focused on protecting data and business content, such as customer orders, secret design documents, and financial records. Preventing leaks and data theft is critical for maintaining your customers’ trust, and for protecting the assets that contribute to your competitive advantage.

Maintaining a strong cloud security posture helps organizations achieve the now widely recognized benefits of cloud computing: lower upfront costs, reduced ongoing operational and administrative costs, ease of scaling, increased reliability and availability, and a whole new way of working.

To actually reap the rewards of these benefits, the journey to the cloud must be secured in such a way, that the proliferation of connected devices coupled with today’s vanishing perimeter and ever-changing threat landscape do not overly complicate an already complex environment for organizations to secure. This is where VMware come in.

VMware allows you to access any cloud while maintaining the highest level of consistency for infrastructure, operations and developer experience. With VMware, you have the freedom to build and deploy modern applications, from the data center to multiple cloud and edge environments. Migrate seamlessly between environments and ensure that all data and applications remain secure and protected in any cloud.

Vmware also allow you to match your applications to the unique strengths of any environment. With the most hybrid cloud options—delivered with key partners like AWS, Azure, Google Cloud, IBM Cloud and Oracle Cloud—you can access the power of leading hyperscale clouds within proven VMware infrastructure.

One critical principle that VMware employ is that the measures shouldn’t be so rigid that users have to find workarounds to do their jobs. When security controls make a cloud computing solution difficult to use, users figure out ways of working around the controls, thereby rendering the system insecure. As experts point out, users are often the weakest link in any security system.

As organizations rapidly adopt cloud networks, due to flexibility, scalability, cost effectiveness and more, security needs to be at the front and centre. It is against this background that the virtual Cloud & Security Forum 2020 is designed to assist in driving an impartial and informed dialogue in the rapidly evolving information security industry. The virtual event will unfold on the 18th and 19th of March.

Having continued to create an environment for making new connections, the summit with visionary speakers at the nexus of new technologies and cyber security solutions purposes to share their insights and demonstrate their ability to safeguard customer data.

From new applications being consistently added to networks to employees taking access control shortcuts and credentials being compromised, it is clear that there are endless vulnerabilities to detect and manage.

From Bespoke Muskets to Interchangeable Software

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

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

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

What Is DevSecOps?

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

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

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

The Three Pillars of DevSecOps

1. Processes

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

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

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

2. Tools

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

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

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

3. People and Organizational Culture

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

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

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

Key Advantages of DevSecOps for Government IT

Shorter Time to Value

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

Faster Fielding of New Capabilities

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

Moving Risk Left

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

Increased Resilience Through Automation

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

Conclusion

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

Why Healthcare IT Is Turning to Database DevOps

The COVID-19 pandemic placed extraordinary pressure on healthcare IT teams in 2020. With budgets constrained, staff stretched thin, and demand for remote patient services surging, engineering organizations were forced to accelerate digital transformation faster than originally planned. According to Redgate Software’s 2020 State of Database DevOps Report for the Healthcare sector, one clear response to this pressure was a measurable increase in DevOps and database DevOps adoption.

The need to rapidly develop and deploy telehealth platforms, provide reliable access to electronic health records (EHR), and maintain strict compliance with data protection regulations pushed healthcare IT teams to standardize processes and lean on automation. Database DevOps — extending software delivery best practices to include the database layer — emerged as a practical answer to these challenges.

Key Findings from the 2020 Healthcare Report

1. DevOps Adoption Is Outpacing Other Sectors

The report found that 70% of healthcare respondents had adopted DevOps across all or some projects, or had a Proof of Concept (PoC) in place — slightly ahead of the cross-industry average of 68%. More significantly, 41% had moved beyond the PoC stage, compared to 36% in sectors such as government and financial services. This suggests that healthcare organizations are not just experimenting with DevOps — they are operationalizing it.

2. Healthcare Leads in Database Automation Adoption

The healthcare sector ranked ahead of other industries in adopting automation across the database development lifecycle. Survey respondents cited the following as the top three drivers for automation:

• Freeing up developer time — cited by 27% of respondents
• Increasing speed of delivery — cited by 22%
• Reducing the risk of data loss — cited by 13%

The emphasis on data loss prevention is notable given the sensitivity of patient health data and the strict regulatory environment healthcare organizations operate in, including requirements under HIPAA in the United States.

3. Cloud Adoption Is Growing but Still Maturing

42% of healthcare respondents reported using cloud infrastructure in some capacity. While this figure is comparable to other industries, Redgate’s analysts noted that cloud adoption in healthcare is likely to accelerate as remote service delivery becomes the norm and DevOps practices make cloud-native deployment more accessible to teams that previously relied on on-premises infrastructure.

4. Version Control Is Becoming Central to Compliance

58% of respondents reported having version control measures in place for database code. Version control for database schemas and scripts enables automated builds and deployments, and serves as a foundational component for compliance auditing. When database changes are tracked in source control, teams can demonstrate repeatability, traceability, and the elimination of ad-hoc manual changes — all of which are critical requirements in regulated industries.

In practice, this means treating database migrations and schema changes with the same rigor as application code: committing changes to a repository, running automated validation, and deploying through a controlled pipeline rather than applying changes directly to production databases.

5. Database Deployment Frequency Is Increasing Significantly

51% of healthcare respondents reported deploying database changes daily or weekly in 2020, a significant increase from previous years. Among North American respondents specifically, 55% said they deploy database changes daily, weekly, or on demand — a figure that points to the rapid adoption of Agile and continuous delivery practices to support frequently changing clinical and operational applications.

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

— Kendra Little, DevOps Advocate, Redgate Software

What Database DevOps Means in a Healthcare Context

For most engineering teams, “DevOps” primarily refers to application code pipelines: continuous integration, automated testing, and continuous deployment. However, the database layer is often excluded from these pipelines, becoming a bottleneck where changes are applied manually by a DBA, are difficult to roll back, and are poorly audited.

Database DevOps addresses this by bringing the same practices to the data tier:

• Source control for schemas: Database objects (tables, stored procedures, views) are stored in version control alongside application code.
• Automated schema comparison and migration: Tooling compares the target database state with the desired state and generates safe migration scripts.
• CI/CD pipeline integration: Database changes are validated and deployed through the same pipeline as application changes, ensuring consistency across environments.
• Drift detection: Automated checks identify when a live database has diverged from its source-controlled definition, a critical capability for compliance.
• Rollback capability: Because changes are scripted and versioned, rolling back a bad deployment is planned for rather than improvised.

In healthcare specifically, these capabilities support compliance frameworks by ensuring that every database change is documented, reviewed, and reproducible — reducing the risk of audit failures or data integrity issues that could compromise patient safety.

The Compliance Dimension

Healthcare organizations must navigate complex regulatory environments. In the United States, HIPAA mandates strict controls over how protected health information (PHI) is stored, accessed, and modified. In Europe, GDPR applies. Many organizations are also subject to additional industry-specific standards.

Database DevOps practices directly support several common compliance requirements:

• Audit trails: Version control history provides a complete record of who changed what and when.
• Access control: Pipeline-based deployments remove the need for developers to have direct write access to production databases.
• Repeatability: Automated deployments are consistent and deterministic, reducing the risk of human error that could introduce vulnerabilities.
• Data masking in non-production environments: Tooling can ensure that sensitive patient data is masked before being used in development or test environments, so PHI never leaves the production boundary unprotected.

Telehealth as a Forcing Function

The rapid expansion of telehealth services during the pandemic created an immediate engineering challenge: systems that were designed for in-clinic use had to scale for remote access, often with little lead time. Backend databases supporting appointment scheduling, patient records, billing, and clinical workflows suddenly needed to handle greater load, more frequent updates, and integration with new front-end applications.

Teams that had already adopted database DevOps were better positioned to respond quickly — deploying schema changes to support new features, adding indexes to handle increased query load, and integrating new data sources through controlled, automated pipelines rather than through emergency manual interventions.

About the Research

Redgate Software has published its State of Database DevOps Report annually for over four years. The 2020 edition gathered responses from developers, database specialists, and IT leaders across multiple industries and company sizes globally. The healthcare sector findings represent a subset of that broader dataset, enabling sector-specific comparisons against cross-industry benchmarks.

For the full healthcare sector findings, refer to Redgate’s official report publications at red-gate.com.

About Redgate Software

Redgate Software, headquartered in Cambridge, England, develops tools for database development and DevOps, with a focus on Microsoft SQL Server environments. The company’s tooling is used by IT professionals across organizations of all sizes to automate database deployments, enforce version control, and maintain compliance. According to Redgate, its tools are used by more than 100,000 companies, including a significant proportion of Fortune 100 organizations.

How many certifications you have and where you live will likely drive what you make as a cloud engineer. Catch the latest cloud salary and hiring trends.

COVID-19 has caused a spike in demand for lucrative cloud-related roles, which could offer engineers an opportunity to take the next step in their careers. However, the adoption of remote working strategies could complicate how professionals are compensated across the IT industry going forward.

The average annual salary for a cloud engineer is just under $129,000, according to ZipRecruiter, an employment marketplace. But this number will vary depending on a few factors, such as where IT pros live and the industries they work in. However, recent industry salary reports indicate that many of the variables that determine cloud computing salaries are in the job candidates’ hands.

For example, a job candidate can boost salary potential by earning relevant certifications or relocating to an emerging tech hub — like Columbus, Ohio or Denver.

Demand for cloud engineers and architects has gone up since the outbreak began, according to Scott Engler, an analyst at Gartner. But the rise of remote work has increased the supply of available talent as well.

“The supply-demand equation just changed,” Engler said. “It allows for more wage arbitrage.”

Remote work also changes the compensation equation around cost of living. A cloud professional now working remotely with a coastal salary may want to relocate to a city with lower living expenses.

Two of the biggest factors that determine your cloud computing salary are how many certifications you have and where you live. Learn how COVID-19 and remote work have altered this equation.

Raise your skills with certifications

IT pros often get certified in specific technologies to improve their skills, but industry certifications are also a popular and affordable way to earn salary bumps.

According to Global Knowledge’s 2019 IT Skills and Salary Report, 85% of IT professionals worldwide — and 81% in North America — have at least one certification. Global Knowledge, a provider of IT skills and training services, examined multiple industries and locations and found that certified professionals earn more than their non-certified counterparts. With each additional certification there is also a small bump in salary. In North America, a certified IT professional earns 7% more than someone who is not certified — the average salary jumps around 2% for each additional certification earned.

Of the 10 certifications that attract the highest salaries in North America, four are cloud related. The Google Cloud Platform Cloud Architect certification is the highest on that list, with certificate earners averaging an annual salary of $152,129. Three different AWS certifications — AWS Certified Solutions Architect – Associate, AWS Certified SysOps Administrator – Associate or AWS Certified Developer – Associate — can translate to salaries around $130,000 a year.

In addition, cross-certifying with a breadth of certifications is a strong way to improve an IT pro’s cloud computing salary, according to Global Knowledge. Cross-certifying with AWS is a particularly attractive option in North America. For example, pairing an AWS certification with one from VMware, Cisco or CompTIA can increase a candidate’s income by more than $10,000.

While in-person certification exams are unavailable at time of publication due the pandemic, each major cloud platform offers certification exams with online proctoring.

The benefits of earning multiple certifications
Relocation is an enticing option, for now
Relocation has long been a way to increase yearly earnings, but that doesn’t mean you should head straight to the San Francisco Bay area. When looking to relocate, cloud professionals need to factor in where salaries are rising, as well as the cost of living in those places.

Cloud professionals just starting out can also pursue opportunities in what Dice, an employment marketplace, calls “emerging tech hubs” in its 2020 Tech Salary Report. In these emerging tech hubs, IT and tech salaries are rising, and cost of living is still relatively low. The highest increases in IT salary have occurred in Columbus, Ohio (14.2%), St. Louis (13.6%), Atlanta (9.5%) and Denver (6.5%).

The 2020 State of Salaries report from Hired, an employment marketplace focused on technology roles, evaluated how far your tech salary would go in certain cities. The report adjusted the average tech salary in major U.S. cities for Bay area living expenses. The report found that adjusted salaries in Austin, Texas and Denver became $224,000 and $202,000, respectively. This explains how, when evaluating whether or not to relocate, candidates should look for how far their money will go, not necessarily how much they could earn.

Remote work brings relocation wrinkle
The rise of remote work has raised a few issues around compensation, especially in terms of cost of living. According to Hired’s report, 53% of tech professionals said a permanent remote working situation would make them likely to move to a city with lower living expenses. If IT pros are working remotely for the foreseeable future, it makes sense for many to move to a more affordable location. However, employers may not want to pay Silicon Valley salaries for employees working from home in Montana.

For example, Facebook said it could shift half of its workforce into remote work within five years, but it would also adjust salaries for cost of living. So, if a Facebook employee moves out of Silicon Valley to work remotely in a more affordable area, the employee’s salary could be reduced.

Unsurprisingly, tech professionals are not on board with altering salaries based on remote work situations. Most tech talent believe remote and in-office employees should be paid the same and would not take a pay cut to work from home on a permanent basis, according to the Hired report.

Remote work has begun to change the way companies seek and compensate tech talent, Engler said. Organizations are now looking to identify pockets of talent around the world and have found them in places like Warsaw, Poland; Chennai, India; Melbourne, Australia; St. Petersburg, Russia; and Mexico City. Remote work offers more opportunity for cloud professionals around the world, but that also means more competition for employment opportunities.

“You’re now going to be competing as a job seeker with someone from Warsaw with some companies,” Engler said.

The tech industry might see wage arbitration on a macro scale over the next year as these conflicts come to a head, Engler said. Employers and job seekers will have to hash out exactly how remote and hybrid work strategies change compensation models over the next year.

Cloud Revenue Surge Drives Demand for Specialist Talent

Leading cloud talent solutions firm Tenth Revolution Group announced in mid-2020 a major recruitment initiative aimed at bringing 400 experienced recruitment consultants on board within 12 months. The expansion was driven by record-breaking revenue announcements from both Microsoft and Amazon Web Services (AWS), combined with a sharp acceleration in enterprise cloud adoption tied to the global shift to remote work.

According to Tenth Revolution Group Chairman and CEO James Lloyd-Townshend, Microsoft reported quarterly revenue of $35 billion at the time — up 15% year-on-year — with Azure recording 59% growth and Dynamics growing 17%. In the same period, AWS surpassed $10 billion in quarterly revenue for the first time, representing 33% year-on-year growth.

“Businesses are fast-tracking cloud transformation projects right now as they adapt to the ‘new normal.’ The incredible demand for AWS and Microsoft in the last quarter has been fuelled by remote working, which also represents huge demand on our services.”

— James Lloyd-Townshend, Chairman and CEO, Tenth Revolution Group

Where the New Roles Are Located

The 400 senior recruitment consultants were targeted for offices across nine US cities:

• Philadelphia
• New York
• Chicago
• Tampa
• Irvine
• Dallas
• Denver
• Charlotte
• Scottsdale

The hires were intended to grow the firm’s two specialist recruitment brands: Nigel Frank (Microsoft-focused) and Jefferson Frank (AWS-focused). Tenth Revolution Group stated it was prioritising candidates with a demonstrated track record in senior sales roles within the recruitment industry, rather than training new entrants to the profession.

Why Senior-Only Hiring for the Rest of 2020

The firm made a deliberate strategic decision to recruit only senior sales consultants for the remainder of 2020. The rationale was speed-to-impact: businesses urgently needed cloud specialists to implement infrastructure changes immediately, not at the end of a graduate training runway.

New consultants joining either Nigel Frank or Jefferson Frank were to complete the company’s virtual onboarding and training programme before taking on a senior recruitment manager role. This approach allowed experienced salespeople to leverage existing skills while acquiring the domain-specific cloud knowledge needed to place candidates effectively.

The Broader Context: Cloud as a Business Continuity Imperative

The 2020 pandemic accelerated digital transformation timelines across virtually every industry. Organisations that had previously treated cloud migration as a medium-to-long-term project found themselves needing to act within weeks to support distributed workforces. This created an acute shortage of cloud architects, DevOps engineers, and platform specialists with hands-on AWS and Azure experience.

“Digital transformation is no longer a future objective. No matter what industry, or size of business, having a cloud infrastructure is critical to ensure your business can operate now. It’s gone from being something low on the to-do list, to requiring specialist hires who can implement change immediately.”

— Zoë Morris, President, Tenth Revolution Group

Major employers reinforced this shift. Twitter, for example, announced that employees would be able to work from home permanently — a signal that enterprises were committing to long-term remote infrastructure rather than treating it as a temporary measure. This type of permanent organisational change translated directly into sustained, rather than one-off, demand for cloud talent.

About Tenth Revolution Group

Tenth Revolution Group operates a portfolio of niche technology staffing brands and training academies. Its recruitment brands cover cloud and enterprise platforms including AWS, Microsoft, Salesforce, ServiceNow, and other cloud-based technologies. At the time of the announcement, the group was backed by TPG Growth, the middle-market and growth equity investment platform of global alternative asset firm TPG.

Key Takeaways for DevOps and Cloud Hiring Teams

• Cloud talent scarcity is structural, not cyclical. The combination of Azure and AWS growth trajectories means demand for skilled practitioners consistently outpaces supply.
• Staffing firms are a leading indicator of market demand. When specialist recruiters hire aggressively, it reflects genuine pipeline pressure from enterprise clients — not speculative expansion.
• Remote-work permanence has changed hiring geography. Organisations no longer need cloud engineers located in a single HQ city, broadening the candidate pool while simultaneously broadening competition for the same specialists.
• Senior experience commands a premium. The deliberate choice to hire only experienced consultants signals that time-to-productivity matters more than cost optimisation in high-growth environments.

The Pandemic as a Forcing Function for Digital Transformation

Almost no business event in recent memory compressed years of change into months the way COVID-19 did. Organisations that had been gradually exploring remote work, CI/CD pipelines and self-service digital channels found themselves deploying all three simultaneously — ready or not.

For technology leaders, the immediate question was whether the collaboration and communication processes they had built would survive the sudden shift to fully distributed teams. DevOps, it turned out, was well-prepared for exactly that test. Its emphasis on automated pipelines, shared tooling, observable systems and asynchronous-friendly workflows meant that many DevOps-mature teams barely broke stride when offices closed.

Why DevOps Is Built for Distributed Work

Traditional software delivery relies heavily on physical proximity — whiteboards, hallway conversations and ad-hoc desk visits. DevOps practices, by contrast, encode collaboration directly into the toolchain:

• Infrastructure as Code means environment configuration is reviewed, versioned and merged through pull requests rather than documented in someone’s notebook.
• CI/CD pipelines provide a shared, automated path to production that every team member can observe and contribute to regardless of location.
• Observability tooling (metrics, logs, traces) gives distributed teams a common view of system health without requiring someone to physically walk over to a server room.
• Incident management platforms route alerts, coordinate responders and capture post-incident learnings in tools that are inherently remote-friendly.

Organisations that had invested in these capabilities before the pandemic found remote work largely frictionless from an engineering perspective. Those still relying on manual handoffs, tribal knowledge or physical sign-off processes discovered quickly how fragile those approaches were.

Surging Digital Demand Raised the Stakes for Speed

At exactly the moment engineering teams went remote, customer demand for digital services spiked. Grocery delivery, telehealth, video conferencing, online banking and e-commerce all saw rapid adoption from customers who had previously used in-person alternatives. According to a survey of 300 consumers cited in the original article — conducted by incident management platform xMatters — 90% reported using digital services more heavily during the pandemic, and 82% said they expected to continue doing so afterward.

That sustained demand shift means engineering teams can no longer treat digital experience as secondary to in-person channels. Customers who moved online out of necessity quickly developed high expectations around uptime, performance and feature velocity. Slow release cycles, lengthy change-approval boards and infrequent deployments are direct competitive liabilities in that environment.

DevOps practices — particularly continuous delivery and automated testing — directly address deployment frequency and lead time, two of the four key metrics identified in the DORA research programme as predictors of both software delivery performance and organisational outcomes.

Building a Unified Team Across Specialisations

One of the persistent challenges in DevOps adoption is that development, QA, security and customer success teams often carry different cultural assumptions about what good work looks like. In a co-located office, informal contact can smooth over some of those differences. Distributed work removes that buffer entirely.

A practical way to create genuine alignment across specialisations is to define shared, measurable objectives that map directly to customer experience. Examples include:

• Availability targets — such as 99.999% (five nines) uptime — give every team a concrete outcome to optimise toward rather than local metrics that may conflict.
• Performance budgets — for example, sub-second page load times — make front-end, back-end and infrastructure work tangibly connected.
• Security SLOs — such as same-day critical patch deployment — integrate security into the delivery pipeline rather than treating it as a gate at the end.

Shared Service Level Indicators (SLIs) and Service Level Objectives (SLOs) also create a common language between engineering and executive stakeholders, making it easier to justify tooling investments and headcount decisions with data rather than intuition.

Executive sponsorship matters here. When senior leadership visibly prioritises DevOps outcomes — allocating budget for platform tooling, reducing low-value on-call toil and recognising cross-functional contributions — teams are far more likely to adopt the cultural shifts that DevOps requires alongside the technical ones.

Developer Burnout: The Hidden Risk in Rapid Scaling

DevOps engineers were already one of the most in-demand roles in the industry before the pandemic. The xMatters survey cited in the original article found that DevOps was the single most recruited position on LinkedIn at the time of writing. Attracting experienced practitioners is difficult; retaining them under sustained pressure is harder still.

The same xMatters survey that asked consumers about digital adoption also asked 300 IT professionals about its impact on their working lives. The result was stark: 88% reported that their work-life balance had deteriorated during the pandemic. DevOps teams, already accustomed to on-call rotations and high deployment frequency, were absorbing additional demand from organisations accelerating their digital programmes at the same time.

Burnout in engineering teams has compounding consequences: increased error rates, higher attrition, institutional knowledge loss and degraded on-call responsiveness. Practical steps to manage the risk include:

• Automating toil — use CI/CD automation and self-healing infrastructure to reduce the volume of manual, repetitive work that falls to engineers.
• Improving on-call hygiene — review alert signal-to-noise ratios, implement escalation policies that respect off-hours time and rotate on-call responsibilities equitably.
• Making work-life balance a stated priority — engineering managers need explicit permission from leadership to protect team capacity rather than simply absorbing every new request.
• Investing in developer experience — reducing friction in the development environment, deployment pipeline and incident response tooling directly reduces cognitive load.

The Business Case for DevOps Remains Strong

The original article cites market research from Global Market Insights indicating that the DevOps market exceeded $4 billion in 2019 and was projected to grow at over 20% compound annual growth rate through 2026. It also references a CA Technologies (now Broadcom) study finding that more agile companies achieve 60% higher revenue and profit growth than peers, and a Gartner study in which 74% of respondents reported better alignment between IT and the business after adopting agile practices.

These figures should be read in the context of their original methodologies, which are not described in detail in the source. That said, the directional conclusion is consistent with independent research: organisations that reduce lead time, increase deployment frequency and lower change failure rates consistently report better business outcomes as well as engineering outcomes.

Companies that deferred DevOps adoption during the pandemic — expecting to return to previous working patterns — are likely to find themselves structurally slower than competitors who used the disruption as an accelerant.

Key Takeaways for Engineering Leaders

• DevOps practices are inherently remote-friendly because collaboration and process are encoded in tooling rather than physical co-location.
• Sustained growth in digital service consumption means deployment velocity and reliability are now front-line business metrics, not internal engineering concerns.
• Cross-functional alignment is most durable when grounded in shared, customer-facing SLOs rather than team-local metrics.
• Developer burnout is a measurable risk during periods of rapid scaling; automation of toil and protection of work-life boundaries are operational priorities, not nice-to-haves.
• Executive sponsorship — expressed through budget, tooling and talent alignment — is a prerequisite for successful DevOps transformation at scale.

APAC Cloud Spending Is Growing Faster Than Any Other Region

According to an April 2020 study by Boston Consulting Group (BCG), developed in collaboration with Amazon Web Services (AWS), spending on public cloud services across the Asia-Pacific (APAC) region is growing at a compound annual growth rate (CAGR) of 25%. That pace significantly outstrips comparable growth in the United States and Western Europe, signaling that APAC enterprises are prioritizing cloud investment as a strategic lever rather than simply a cost-reduction measure.

The study—titled “Businesses in Asia-Pacific Can Find Resilience and Growth in the Cloud”—covers eight nations: Australia, India, Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam. It found that companies in these markets are allocating an average of approximately 5% of their IT budgets to cloud expenditure, a figure the report projected to double by 2023.

Estimated Economic Impact: More Than $450 Billion

The BCG report estimated that growth in public cloud adoption could generate a total economic impact of more than US$450 billion across six major APAC markets—Australia, India, Indonesia, Japan, Singapore, and South Korea—between 2019 and 2023. The study cited real-world examples demonstrating how cloud-based analytics can drive measurable operational savings, including a case involving an Australian airline that reportedly saved $40 million per year in fuel costs by using cloud analytics to optimize flight routes.

The report also highlighted the role of the COVID-19 pandemic as a catalyst for accelerated cloud adoption. Companies across the region rapidly shifted workforces to virtual environments, deployed end-user computing solutions, and used cloud infrastructure to roll out collaboration tools at speed and scale.

“One of the main benefits of cloud computing is access to innovation and agility. We’re seeing in the current COVID-19 crisis that more companies are shifting workers to virtual environments and employing end-user computing, using the cloud to quickly roll out essential collaboration tools. Cloud investments will have longer-term benefits as well, such as risk reduction, the ability to provide new and better services to business stakeholders, and improved personalization of product and service offerings.”

— Luc Grimond, Managing Director, BCG

The Shift From Cost-Cutting to Growth Strategy

A notable finding of the BCG study is the shift in how APAC organizations frame their cloud investments. Traditionally, the primary driver of cloud adoption has been the variabilization of IT costs—replacing capital-intensive on-premises data centers with flexible, consumption-based pricing. However, the survey found that a large majority of APAC respondents are now pursuing cloud adoption explicitly as a growth enabler, not just a cost-efficiency mechanism.

This framing matters for DevOps and platform engineering teams, because it changes the internal mandate. Teams are increasingly being asked to support faster product delivery, better customer personalization, and new service models—not only to reduce infrastructure spend. Cloud-native architectures, CI/CD pipelines, and managed services all become more strategically justified when the organization views the cloud as a revenue driver.

Barriers to Full Cloud Adoption in APAC

Despite strong momentum, the BCG study identified several structural barriers slowing cloud adoption in the region:

• Weak business-IT alignment: Many organizations in the region still operate with siloed IT and business functions. Even where business leaders understand that cloud can accelerate service deployment by 30–60%, that understanding does not always translate into coordinated investment or governance decisions.
• Immature cloud operating models: Outside of financial services and industrial goods sectors, most companies have not updated their monitoring, procurement, risk management, or governance frameworks to accommodate cloud-specific practices. Without these foundations, integrating cloud capabilities into core business processes becomes inconsistent and difficult to sustain.
• Talent gaps: There is a shortage of IT professionals with deep engineering and coding expertise who can design and implement cloud use cases tied to tangible business outcomes. This skills gap slows both migration and value realization.
• Legacy infrastructure inertia: Companies that have not yet modernized their core systems often find it difficult to justify migration costs in the short term, particularly when on-premises systems appear to be functioning adequately.

What DevOps Teams Can Take From This

For engineering and platform teams operating in the APAC region—or building products for organizations there—the BCG findings have practical implications:

• Cloud investment is accelerating: Budget is increasingly available for cloud-native tooling, automation, and infrastructure modernization. Teams that can demonstrate business impact—not just uptime metrics—will be better positioned to secure that investment.
• Governance is a bottleneck: Building cloud operating models (FinOps practices, security guardrails, compliance automation) is often the rate-limiting factor, not technical capability. DevOps teams that can operationalize governance will reduce friction for the broader organization.
• Skills development is critical: Organizations that invest in internal cloud engineering capabilities—rather than relying purely on vendor-managed services—tend to achieve better long-term outcomes. Cross-training, internal guilds, and structured cloud certification programs can close talent gaps faster than external hiring alone.
• COVID-19 as an inflection point: The pandemic demonstrated that organizations with mature cloud infrastructure could adapt faster. This has created organizational memory and urgency that persists even as immediate crisis conditions ease.

Summary

The 2020 BCG/AWS study paints a clear picture: APAC is one of the fastest-growing public cloud markets in the world, driven by a combination of digital transformation ambition, pandemic-driven urgency, and a fundamental reframing of cloud as a growth tool rather than purely a cost play. The barriers—talent shortages, legacy debt, and governance gaps—are real but solvable, and organizations that address them systematically stand to capture a disproportionate share of the economic opportunity the study identified.

For DevOps practitioners, this environment means more mandate, more budget, and more organizational pressure to deliver cloud outcomes at speed—making disciplined platform engineering and sound operating model design more important than ever.

Overview

AppDynamics published findings in May 2020 revealing a significant spike in demand from IT professionals for Application Performance Management (APM), cloud monitoring, and microservices support during the early months of the COVID-19 pandemic. The company analyzed organic web traffic to its own site, trending search terms, training course enrollment, and frequently asked customer questions across a defined window — March 1 to April 4, 2020 — to identify where practitioners were seeking help most urgently.

The findings were not independent market research but an analysis of AppDynamics’ own platform data and website behavior, so they reflect the needs of its existing audience and prospects rather than the broader industry as a whole. With that context in mind, the trends they identified are consistent with the pressures IT organizations faced as remote work, online commerce, and telehealth scaled almost overnight.

The Context: Digital Services Under Unprecedented Load

The shift to remote work, e-commerce, online education, and telehealth during 2020 placed extraordinary demand on enterprise IT infrastructure. Applications that had previously handled moderate or predictable traffic suddenly became the sole channel through which organizations delivered value to both employees and customers.

AppDynamics cited its own pre-pandemic research finding that 71 percent of consumers said digital services were so embedded in daily life that they rarely paused to consider how much they depended on them. When physical channels closed, that latent dependency became explicit — and the quality of digital experiences became a direct business differentiator.

“Applications have quickly become the gateway to the critical digital services we now rely on to function day to day, both for personal and professional needs. This means application owners are getting hit with record usage, while under intense pressure to get products, services and information to their customers quickly — all while maintaining a great digital experience.”

— Angie Mistretta, Chief Marketing Officer, AppDynamics

Trend 1: Rising Demand for Application Performance Management

As application traffic surged and user expectations rose, IT teams increasingly searched for guidance on monitoring and managing application performance. AppDynamics reported the following signals from its data:

• Search terms including “APM”, “Application Performance Monitoring”, and “Digital Experience Monitoring” ranked in the top five most frequently searched terms globally among visitors to the AppDynamics site.
• Pages covering end-to-end APM strategy saw a 35% increase in web traffic during the March 1–April 4 window.
• Resources focused on application performance testing saw a 24% traffic increase over the same period.
• Enrollment in AppDynamics’ Core APM class through its Premium University program increased by 233%.

The steep jump in training enrollment is particularly notable. Practitioners were not just reading about APM concepts — they were committing time to structured learning, indicating a recognition that existing monitoring practices were insufficient for the scale and criticality of applications under pandemic conditions.

For DevOps teams, APM in this context means full-stack visibility: tracing transactions from the end-user browser or mobile device through APIs, microservices, databases, and network infrastructure. Without that visibility, diagnosing the root cause of degraded user experience at scale becomes guesswork.

Trend 2: Cloud Monitoring Becomes Critical Infrastructure

The pandemic invalidated capacity planning models built around historical patterns. Demand curves that had taken months or years to develop shifted in days, and organizations needed the ability to scale compute resources rapidly — something on-premises infrastructure cannot easily accommodate.

AppDynamics reported a more than 40% increase in traffic to cloud monitoring pages on its website. Search terms trending among its audience included “cloud operations”, “Azure”, and “AWS monitoring”.

For organizations still running primarily on-premises workloads, the immediate challenge was enabling a remote workforce to securely access business-critical systems in real time. Cloud infrastructure — whether through migration or hybrid deployment — offered the elasticity those organizations needed. Monitoring that cloud infrastructure then became an immediate operational priority rather than a future-state roadmap item.

Key cloud monitoring concerns in this environment typically include:

• Auto-scaling behavior and cost visibility across cloud providers
• Latency and availability of cloud-hosted APIs and services
• Correlating cloud infrastructure metrics with application performance data
• Multi-cloud and hybrid environments where workloads span both on-premises and cloud resources

Trend 3: Microservices and Kubernetes Adoption Accelerates

Speed of delivery became a survival requirement for many businesses in early 2020. Developers needed to ship new features, fix critical bugs, and scale services faster than traditional monolithic application architectures permit. Microservices — decomposing applications into small, independently deployable services — offer the development velocity and horizontal scalability that teams needed.

AppDynamics observed a 21% increase in traffic to pages covering microservices adoption challenges and implementation guidance. Related trends included:

• Traffic to Kubernetes monitoring resources increased by 20%.
• Resources on best practices for instrumenting containers saw a 33% traffic increase.

These numbers reflect a well-known operational challenge: microservices architectures significantly increase observability complexity. Where a monolith might generate a single log stream and a handful of metrics, a microservices deployment running dozens or hundreds of services generates distributed traces, per-service metrics, and inter-service dependencies that require purpose-built tooling to understand.

Kubernetes adds another layer: container scheduling, resource allocation, pod health, and node-level metrics all require monitoring in addition to the application-layer instrumentation teams would apply in any architecture. The interest in instrumentation best practices suggests teams were not just deploying containers but actively working to make those deployments observable in production.

What This Means for DevOps Teams

The three trends AppDynamics identified — APM adoption, cloud monitoring, and microservices observability — are closely interrelated. Organizations accelerating cloud migration to enable remote work often simultaneously adopt containerized, microservices-based architectures to gain deployment agility. Both shifts increase monitoring complexity, which drives demand for APM tooling capable of providing unified, correlated visibility across distributed systems.

Practically speaking, the implications for DevOps teams include:

• Instrument early: Adding APM agents or OpenTelemetry instrumentation at deployment time is significantly less disruptive than retrofitting a production system under load.
• Define SLOs before incidents happen: Service Level Objectives give teams a clear threshold for acceptable performance and a shared definition of what “degraded” means before users start complaining.
• Correlate infrastructure and application metrics: Cloud resource saturation (CPU, memory, network) and application-layer latency need to be visible in the same context to diagnose root causes quickly.
• Invest in distributed tracing: In microservices environments, request traces that span multiple services are often the only way to identify exactly where in a call chain latency or errors are introduced.
• Treat monitoring as a team skill: The 233% jump in APM training enrollment suggests organizations recognized that tooling alone is insufficient — practitioners need to understand how to use it effectively.

“IT professionals need additional support and tools in order to combat these new challenges, because they know the quality of the digital experiences they deliver today will impact their success both now, and after the pandemic subsides.”

— Angie Mistretta, AppDynamics CMO

Summary

AppDynamics’ self-reported data from March–April 2020 documented a measurable surge in practitioner interest across APM, cloud monitoring, and microservices observability. While the data reflects AppDynamics’ own audience and should not be extrapolated as industry-wide benchmarks, the underlying pressures it describes — rapidly scaling digital traffic, accelerated cloud adoption, and the observability challenges of distributed architectures — are well-documented phenomena that continued well beyond the initial pandemic period. Teams that invested in monitoring infrastructure during this period were better positioned to maintain service reliability as digital-first operating models became permanent.

The State of Cloud Security: Still Dangerously Immature

A 2020 joint report from Oracle and KPMG painted a sobering picture of cloud security preparedness: 92% of IT professionals surveyed did not believe their organization was well-prepared to secure public cloud services. The survey, conducted by Enterprise Strategy Group, polled 750 cybersecurity and IT professionals across North America, Western Europe, and Asia-Pacific between December 2019 and January 2020.

The two most critical risks identified were over-privileged admin accounts and poor management of cloud secrets — including API keys, account credentials, and passwords. The report’s core message is clear: security in the cloud is failing not because of exotic attacks, but because of avoidable misconfigurations and cultural blind spots that DevSecOps practices are specifically designed to address.

Key Findings From the Cloud Threat Report 2020

The report surfaced several recurring themes across the organizations surveyed:

• Cybersecurity teams are consistently playing catch-up with cloud adoption.
• Fundamental cloud security concepts are not yet widely understood.
• Misconfigured cloud services are prevalent and represent the top security priority.
• Improving security requires changes to people and processes, not just tooling.
• Machine learning is increasingly viewed as a foundational cybersecurity technology.

Over-Privileged Accounts: The Biggest Misconfiguration Problem

Privileged cloud credentials have become the primary entry point for attackers. The report found that 59% of respondents had team members with privileged cloud accounts whose credentials were compromised via spear-phishing attacks. When attackers obtain privileged credentials, they can pivot to additional cloud secrets and from there access databases, object stores, and other sensitive services.

The top misconfigured cloud services, ranked by respondents, were:

• Over-privileged accounts — 37%
• Exposed web servers and server workloads — 35%
• Object store data not secured via access control lists — 34%
• Lack of multi-factor authentication (MFA) — 33%
• Disabled logging for cloud activity audit trails — 31%

The report recommends implementing least-privilege access policies across all cloud environments. In multi-cloud setups, this is particularly challenging due to the complex many-to-many relationships between users, accounts, and cloud platforms — but it remains essential.

Cloud Secrets Stored in Dangerous Places

A closely related problem is where cloud secrets — keys, tokens, passwords — end up being stored. Development teams moving fast frequently prioritize delivery speed over security hygiene, leaving secrets in plaintext in locations that are far too accessible. Survey respondents reported secrets being found in:

• Stored on servers — 59%
• In source code repositories — 55%
• In public cloud object stores — 54%
• Embedded in HTML code — 31%

The report identifies this as a direct consequence of competing priorities: development velocity versus security discipline. The recommended solutions are implementing least-privilege policies combined with a hardware security module (HSM) or a dedicated secrets vault — such as HashiCorp Vault or a cloud-native equivalent — to manage and rotate secrets programmatically rather than storing them in plaintext.

DevOps Is Mainstream — But Security Integration Is Lagging

One genuinely positive finding from the report is that DevOps adoption has moved well beyond cloud-native startups. Only 6% of respondents said they had no plans to adopt DevOps, while nearly one-third had already implemented it, almost another quarter planned to do so within 12–24 months, and a further third expressed interest.

The problem is that security integration into those DevOps workflows is significantly behind. Just over one-third of respondents said their organization had already integrated security into their DevOps processes. The majority are still treating security as a downstream concern rather than embedding it into the design and build phases — a missed opportunity that leaves known vulnerabilities unaddressed until it is costly to fix them.

What DevSecOps Actually Means in Practice

DevSecOps is not a product — it is a cultural and procedural shift that embeds security into every phase of the software delivery lifecycle. The report describes this as “shifting security left”: moving security testing, policy enforcement, and threat modeling into the development and build phases rather than bolting it on at the end.

To build a DevSecOps pipeline that automates security controls through the CI/CD toolchain, organizations should look at integrating security into:

• Software Development Lifecycle (SDLC) tools, including IDEs with security linting and static analysis plugins.
• Source Code Management (SCM) repositories — with automated secret scanning (e.g., git-secrets, GitHub Advanced Security) to catch credentials before they are committed.
• Automated build tools — incorporating static application security testing (SAST) and dependency vulnerability scanning in CI pipelines.
• Agile project management systems — treating security requirements as first-class backlog items alongside features.
• Collaborative messaging platforms — enabling security alerts and incident notifications to flow directly to development teams.

According to the report, 46% of respondents cited “baking security into every stage of the continuous delivery toolchain” as the most important reason to adopt a DevSecOps approach. Collaboration and efficiency were ranked next, followed by compliance requirements.

Security as a Shared Responsibility, Not an Afterthought

The report’s most important cultural insight is that cloud security cannot succeed as a siloed function. When security is the responsibility of a single team operating at the end of the delivery pipeline, it becomes a bottleneck and an afterthought. Effective DevSecOps requires security to be treated as a shared business requirement — understood and acted on by developers, operations engineers, and security professionals alike.

Practically, this means:

• Training developers to understand the security implications of how and where they store secrets.
• Providing guardrails (automated policy checks, secrets managers, IAM templates) rather than just guidelines.
• Making security metrics visible in the same dashboards where delivery metrics are tracked.
• Conducting threat modeling during sprint planning, not during a separate security review cycle.

Summary

The 2020 Oracle/KPMG Cloud Threat Report makes clear that cloud security failures are largely self-inflicted: over-privileged accounts, secrets stored in source code, disabled logging, and absent MFA are all fixable problems. The barrier is cultural and organizational, not technical. DevSecOps provides the framework to close these gaps by integrating security into the same workflows, pipelines, and tools that development and operations teams already use. Organizations that treat security as a bolt-on will continue to fall behind — those that shift it left into everyday engineering practice will be in a materially stronger position.

Survey methodology: Data was collected via an online survey by Enterprise Strategy Group of 750 cybersecurity and IT professionals from private- and public-sector organizations in the US, Canada, UK, France, Australia, Japan, and Singapore, between December 16, 2019, and January 16, 2020. Respondents were required to be responsible for evaluating, purchasing, and managing cybersecurity technology and to have high familiarity with their organization’s public cloud usage.

Why AWS Skills Are Still Worth Your Time

Cloud adoption has reshaped IT infrastructure management. Organisations that once ran on-premises server rooms now rely on managed cloud platforms to handle compute, storage, networking, and virtually every other infrastructure concern. Amazon Web Services (AWS) remains one of the dominant platforms in that space, with a broad service catalogue covering everything from virtual machines and managed Kubernetes to serverless functions and machine-learning pipelines.

For DevOps engineers and systems administrators, this shift has a direct career implication: cloud fluency is no longer optional. Teams are expected to provision infrastructure, manage IAM policies, configure VPCs, debug deployment pipelines, and optimise cost — all within a cloud environment. Formal training and recognised certifications are practical ways to demonstrate that competence to employers.

What a Multi-Level AWS Bootcamp Typically Covers

Bundled AWS training packages — often marketed as “bootcamps” — usually stack several courses together to move learners from foundational concepts through to more advanced, production-relevant skills. A well-structured bundle aimed at intermediate-to-advanced learners commonly covers:

• AWS Console and account management — navigating the management console, setting up billing alerts, and understanding regions and availability zones.
• Compute services — launching and configuring EC2 instances, understanding instance families, and using Auto Scaling groups.
• Networking fundamentals — creating and segmenting VPCs, configuring security groups, and setting up load balancers.
• Storage and databases — working with S3, EBS, RDS, and DynamoDB, including backup and lifecycle policies.
• Serverless and containerised workloads — deploying applications with AWS Lambda, API Gateway, ECS, or EKS.
• Application deployment — using Elastic Beanstalk or CodeDeploy to ship Node.js, Python, or Java applications.
• Security and IAM — writing least-privilege IAM policies, configuring MFA, and using AWS Organizations.
• Certification preparation — practice questions and exam strategy for credentials such as the AWS Certified Developer – Associate or AWS Certified Solutions Architect – Associate.

Understanding the “90% Off” Pricing Model

E-learning marketplaces and bundle sites frequently advertise large percentage discounts against a stated “original value.” It is worth understanding how these numbers are calculated before treating them as a direct comparison to alternatives.

• Stated retail price vs. actual market price — the “original value” is often a list price that courses are rarely sold at. Compare the bundle price to what the same courses cost individually on the platform at the time of purchase.
• Content currency — AWS releases new services and deprecates features regularly. Check when each course in the bundle was last updated before committing.
• Instructor credentials — look for instructors who hold current AWS certifications or have verifiable industry experience, not just high review counts.
• Access duration — some bundles offer lifetime access; others expire after a set period. Confirm this before purchasing.
• Certificate of completion vs. certification prep — a course certificate of completion is not the same as an AWS certification. Bundles can prepare you for the exam, but you still need to sit and pass the official AWS exam separately.

Who Benefits Most from This Type of Training

Bundled online bootcamps suit a specific learner profile. They tend to deliver the best return on investment when the learner:

• Already works in a technical role (sysadmin, developer, or junior DevOps engineer) and needs structured exposure to AWS services.
• Cannot easily attend instructor-led training due to schedule or budget constraints.
• Is self-disciplined enough to work through video modules without external deadlines.
• Wants a broad overview of AWS before specialising, or is targeting a specific Associate-level certification.

Experienced cloud engineers who already hold multiple AWS certifications are less likely to find significant new value in a general-purpose bundle. Purpose-built advanced courses, official AWS training (available through AWS Training and Certification), or hands-on labs via platforms like AWS Skill Builder may be more appropriate at that level.

Alternatives Worth Comparing

Before purchasing any third-party bundle, it is worth comparing against several well-known options:

• AWS Skill Builder — AWS’s own learning platform, which offers a free tier and a paid subscription with labs, exam readiness courses, and official content.
• A Cloud Guru / Pluralsight — subscription-based platforms with structured AWS learning paths and integrated cloud sandboxes for hands-on practice.
• Udemy — individual AWS courses by well-known instructors (such as Stephane Maarek or Adrian Cantrill) are regularly discounted and widely used for certification prep.
• Linux Foundation and CNCF — useful if your AWS learning is part of a broader cloud-native or Kubernetes skill development plan.

Making the Most of Online AWS Training

Video-based learning alone is rarely sufficient to pass AWS certification exams or perform confidently in a production environment. Supplement any course bundle with the following practices:

• Use the AWS Free Tier — create a personal AWS account and deploy the services you are learning about. Hands-on experience accelerates retention significantly.
• Read the official AWS documentation — AWS documentation is detailed and freely available. Cross-reference what courses teach against the source documentation.
• Practice with sample exam questions — AWS publishes official sample questions for each certification. Third-party question banks (Tutorials Dojo, Whizlabs) are also widely used.
• Set a study schedule — self-paced courses are easy to deprioritise. Block dedicated time and set a target exam date to maintain momentum.
• Join a community — communities such as the AWS subreddit, the Cloud Resume Challenge Discord, and local AWS user groups provide support, accountability, and real-world context.

Bottom Line

Discounted AWS training bundles can represent genuine value, particularly for IT professionals who need structured, self-paced learning on a budget. The key is to evaluate each bundle on the quality and currency of its content, the credibility of its instructors, and how well it aligns with your specific certification or skill goals — rather than on the size of the stated discount alone. AWS expertise continues to be one of the most marketable skills in the DevOps and cloud engineering job market, making any investment in quality training worthwhile over the long term.

TechGig and Google Cloud Partner on Cloud DevJam

TechGig, in collaboration with Google Cloud, launched Cloud DevJam — an interactive online upskilling program aimed at helping Indian IT professionals strengthen their cloud computing skills. The initiative was announced in May 2020, during a period when COVID-19 lockdowns made remote, self-paced learning especially relevant for the developer community.

The program was designed with a specific goal: enable working developers to build practical Google Cloud Platform (GCP) skills without leaving home, while also preparing them for formal Google Cloud certification.

What Cloud DevJam Covers

Cloud DevJam covers a broad range of Google Cloud Platform skill areas, including:

• Core infrastructure — foundational GCP services, compute, networking, and storage
• Data and machine learning — working with BigQuery, AI/ML APIs, and data pipelines
• Application development — building and deploying apps on GCP using services such as App Engine, Cloud Run, and Kubernetes Engine

The curriculum was structured to take participants from foundational knowledge through to more advanced, job-relevant GCP skills.

Program Format: Live Sessions, Labs, and Hackathons

Cloud DevJam is not a passive video course. The program combines several interactive learning formats to keep participants engaged and to validate learning outcomes:

• Live interactive sessions led by Google Cloud subject matter experts
• Multiple choice question (MCQ) tests to assess conceptual understanding
• Hands-on labs for practical, real-environment skill building
• Hackathons and skill tests to apply knowledge under realistic conditions

Participants who complete the program are also given the opportunity to pursue Google Cloud certification, providing a recognized, verifiable credential to add to their professional profile.

Why This Program Was Launched

The rationale behind Cloud DevJam reflects both the immediate context of the 2020 pandemic and longer-term workforce trends in India’s IT sector.

“The COVID-19 outbreak, and the social lockdown, has made it critically important for everyone to remain updated with the newer technologies. Today, the digital frontier — led by cloud-based technologies — is driving most initiatives in all the sectors. Cloud DevJam has been curated to help the developers learn and understand the fundamentals of Google Cloud and advance their skills in the process.”

— Sanjay Goyal, Business Head, TimesJobs and TechGig

“The Cloud DevJam is unique because it’s been conceptualized to enable developers to build their knowledge while at home, and companies will need to call on their skills in the immediate and long term to ensure business agility, resilience, and overall competitiveness.”

— Karan Bajwa, Managing Director, Google Cloud India

Context: Cloud Skills and the Indian IT Workforce

India has one of the largest concentrations of IT professionals in the world, and demand for cloud-certified engineers has grown significantly as enterprises across sectors accelerate their migration to cloud infrastructure. Programs like Cloud DevJam address a real skills gap: many developers have strong software engineering fundamentals but limited hands-on experience with managed cloud services, distributed data platforms, and cloud-native application patterns.

Structured programs that combine live expert instruction with lab environments and recognized certification paths offer a more effective upskilling route than self-directed study alone — particularly for professionals who need to demonstrate cloud competency to current or prospective employers.

Google Cloud Certification Pathway

One of the distinguishing features of Cloud DevJam is the direct link to Google Cloud’s official certification program. Google Cloud offers a tiered certification track, from foundational to professional and specialty levels. Relevant certifications for developers participating in a program like Cloud DevJam would typically include:

• Google Cloud Digital Leader (foundational)
• Associate Cloud Engineer
• Professional Cloud Developer
• Professional Data Engineer
• Professional Machine Learning Engineer

Earning a Google Cloud certification provides independently validated proof of skill that is recognized by employers across cloud-first and cloud-migrating organizations globally.

Where to Learn More

For current upskilling opportunities on Google Cloud, visit the Google Cloud Training portal. TechGig periodically runs similar collaborative programs; check TechGig.com for active challenges and learning initiatives.

Overview

TechGig, in collaboration with Google Cloud, launched Cloud DevJam — an interactive online upskilling program aimed at helping Indian IT professionals strengthen their Google Cloud Platform (GCP) skills. The initiative was announced in May 2020, timed to help developers productively use time at home during COVID-19 lockdowns, and was designed to support both immediate reskilling needs and longer-term career advancement.

What Is Cloud DevJam?

Cloud DevJam is a structured, multi-format learning program built around Google Cloud Platform competencies. Rather than offering passive video content alone, the program combines several learning modalities to reinforce practical understanding:

• Live interactive sessions led by Google Cloud subject-matter experts
• Hands-on labs providing direct experience with GCP tools and services
• Multiple-choice assessments to validate conceptual knowledge
• Hackathons and skill-based contests to apply learning to real-world scenarios

Participants accumulate points across these activities, with top performers earning the recognition of Cloud DevJam Champion.

Skills Covered

The program covers a range of Google Cloud Platform domains, enabling developers to build breadth as well as depth. Key areas include:

• Core Infrastructure — foundational GCP services including compute, storage, and networking
• Data & Machine Learning — working with GCP data services and ML tooling
• Application Development — building and deploying cloud-native applications on GCP

Google Cloud Certification Pathway

Beyond skill-building activities, all Cloud DevJam participants have the opportunity to pursue and complete an official Google Cloud certification. This gives the program a tangible, credential-backed outcome — not just practical knowledge, but a verifiable qualification recognised by employers evaluating cloud-ready talent.

Why This Program Was Launched

Both TechGig and Google Cloud positioned Cloud DevJam as a direct response to accelerating demand for cloud skills, made more urgent by the shift to remote work and digital-first operations during the pandemic.

“The COVID-19 outbreak, and the social lockdown, has made it critically important for everyone to remain updated with the newer technologies. Today, the digital frontier — led by cloud-based technologies — is driving most initiatives in all sectors.”

— Sanjay Goyal, Business Head, TimesJobs and TechGig

“One thing that recent events have made clearer than ever is the importance for organizations and tech talent to be digital-first and cloud-ready. The Cloud DevJam is unique because it’s been conceptualized to enable developers to build their knowledge while at home, and companies will need to call on their skills in the immediate and long term to ensure business agility, resilience, and overall competitiveness.”

— Karan Bajwa, Managing Director, Google Cloud India

Who Should Participate?

Cloud DevJam is aimed primarily at Indian IT professionals — including software developers, cloud engineers, data engineers, and ML practitioners — who want to build or validate their GCP skills. The program’s range of difficulty levels and topic areas makes it suitable for developers at different stages of their cloud learning journey, from those just beginning with GCP fundamentals to those preparing for certification.

How to Get Started

At the time of launch, interested participants were directed to the Cloud DevJam registration portal via TechGig’s platform. If you are reading this after the original launch window, check TechGig’s website and Google Cloud Training for current availability of similar programs, learning paths, and certification preparation resources.

The Broader Context: Cloud Skills in High Demand

The Cloud DevJam launch reflects a broader industry trend: demand for verified cloud skills — particularly on major platforms like GCP, AWS, and Azure — has grown substantially as organisations accelerate digital transformation. Programs that combine structured learning, hands-on labs, and recognised certifications have become a practical route for developers to differentiate themselves and for organisations to build internal cloud capability quickly.

For DevOps engineers and developers considering GCP upskilling today, Google Cloud’s official training and certification catalogue and Qwiklabs (now Google Cloud Skills Boost) offer hands-on lab environments comparable to what Cloud DevJam provided.

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.

TechGig in collaboration with Google Cloud launched the Cloud DevJam, an interactive upskilling online program designed to help Indian IT professionals enhance their cloud-based skills and apply knowledge to advance their professional careers. The program has been designed to enable developers to build their skills while at home during the current scenario.

Cloud DevJam program participants can build their expertise around an array of Google cloud Platform (GCP) skills including Core Infrastructure, Data & Machine Learning, Application Development and more. With live interactive sessions led by Google Cloud experts, developers can apply and test their knowledge through multiple choice question tests, hands-on labs, hackathons, and other skill-tests. Every participant of the program will also have the opportunity to validate their knowledge by applying and completing their Google Cloud certification. With points to be earned with each activity, developers can earn the recognition of ‘Cloud DevJam Champions’ at the end of the program.

Sanjay Goyal, Business Head, TimesJobs and TechGig said, “The COVID-19 outbreak, and the social lockdown, has made it critically important for everyone to remain updated with the newer technologies. Today, the digital frontier – led by the cloud-based technologies – is driving most initiatives in all the sectors. I am glad that at such juncture, TechGig – in collaboration with Google Cloud – is launching ‘Cloud DevJam’, a unique opportunity for the technologists to explore, learn and upskill on the Google Cloud Platform. This program has been curated to help the developers learn and understand the fundamentals of Google Cloud and advance their skills in the process. I welcome all the IT professionals to participate in the ‘Cloud DevJam’ hackathons and skill-based contests and showcase their innovative ideas”.

Karan Bajwa, Managing Director, Google Cloud India said, “One thing that recent events has made clearer than ever is the importance for organisations and tech talent to be digital-first and cloud-ready. The Cloud DevJam is unique because it’s been conceptualized to enable developers to build their knowledge while at home, and companies will need to call on their skills in the immediate and long term to ensure business agility, resilience and overall competitiveness.”

The Missing Piece in Most DevOps Transformations

Most enterprise DevOps initiatives invest heavily in toolchains, automation pipelines, and cloud infrastructure—but according to Jayne Groll, CEO of the DevOps Institute, that focus is dangerously lopsided. While digital transformation dominates the conversation, Groll argues that the industry urgently needs a human transformation to match it.

“Human transformation is the single most critical success factor to enable DevOps practices and patterns for enterprise IT organizations. Traditional upskilling and talent development approaches won’t be enough for enterprises to remain competitive because the increasing demand for IT professionals with core human skills is escalating to a point that business leaders have not yet seen in their lifetime. We must update our humans through new skill sets as often, and with the same focus, as our technology.”

— Jayne Groll, CEO, DevOps Institute

What the Upskilling 2020 Report Found

The DevOps Institute’s Upskilling 2020: Enterprise DevOps Skills Report—based on responses from more than 1,200 practitioners and business leaders—paints a clear picture of where the talent gap is most painful:

• More than 50% of respondents reported experiencing difficulties on their DevOps transformation journeys.
• 58% said finding skilled DevOps professionals is a significant challenge.
• 48% find it difficult to retain skilled DevOps talent once hired.

Compounding the problem, organizational investment in upskilling remains inadequate:

• 38% of respondents work in organizations that have no upskilling program at all.
• 21% are working toward implementing one.
• 7% don’t even know whether an upskilling program exists in their organization.
• Only 31% reported that their company already has a formal upskilling program in place.

The Three Pillars of the DevOps Human

The report identifies the top skill categories needed to develop what the DevOps Institute calls a “DevOps Human”:

1. Process skills and knowledge — Understanding methodologies like Scrum, Value Stream Mapping, and Lean principles.
2. Automation — Proficiency with CI/CD pipelines, infrastructure as code, and test automation.
3. Human skills — Collaboration, interpersonal communication, empathy, and creativity.

Notably, human skills are not an afterthought in this framework—they appear alongside automation as a top-tier requirement, a significant shift in how technical roles are being evaluated.

From T-Shaped to E-Shaped: Evolving the Model

The 2019 DevOps Institute skills report introduced the concept of the T-shaped professional: someone with deep expertise in one discipline (the vertical stem of the T) combined with broad, general knowledge across adjacent areas (the horizontal bar). This model was positioned as the answer to the growing demand for speed, agility, and software quality.

The 2020 report evolves this further, introducing the E-shaped DevOps human. The “E” framework is built around four dimensions, referred to as the 4-Es:

• Experience — Practical, hands-on application of skills in real environments.
• Expertise — Deep, specialized knowledge in one or more areas.
• Exploration — Curiosity-driven learning and a willingness to experiment.
• Execution — The ability to deliver results and drive work to completion.

Within the E-shaped model, the report distinguishes between two categories of skills:

Horizontal Skills (Breadth)

• Automation
• Functional skills
• Knowledge management
• Technical proficiency

Vertical Skills (Depth)

• Flow and systems thinking
• Practice-specific knowledge (e.g., Scrum, Value Stream Mapping)
• Human skills: collaboration, interpersonal communication, empathy, and creativity

Why Human Skills Are Gaining Priority

Eveline Oehrlich, Research Director at the DevOps Institute, underscores that the increased emphasis on human skills isn’t coming from practitioners alone—it’s coming from management:

“We already saw a significant demand for a variety of human must-have skills in our 2019 research and this year we saw a tremendous increase across all human skills—collaboration, interpersonal skills, empathy and creativity, to name a few. The most important though is the increase in value placed on the human skills, which comes from the management and business leaders in our survey. Our research shows that a mindset shift is happening with the transition from ‘soft’ skills to ‘human’ skills, but more importantly, today’s leaders must change their mindset to recognize the value human skills will bring to a team and organization.”

— Eveline Oehrlich, Research Director, DevOps Institute

The rebranding of “soft skills” as “human skills” is deliberate and meaningful. The term “soft” has historically implied these capabilities are secondary or supplementary. By reframing them as “human skills,” the DevOps Institute is positioning collaboration, empathy, and creativity as core professional competencies—on equal footing with technical and automation knowledge.

What This Means for DevOps Teams in Practice

If you’re leading a DevOps team or shaping an engineering organization, the report’s findings translate into several concrete priorities:

• Audit your upskilling program. If your organization is among the 38% with no formal program, that gap is costing you in recruitment difficulty and retention risk.
• Expand your definition of a strong candidate. Hiring managers who filter exclusively for technical certifications or tooling experience may be passing over candidates with the collaboration and systems-thinking skills that make DevOps teams function well.
• Invest in cross-functional learning. The E-shaped model values exploration and breadth as much as depth. Encourage engineers to learn about adjacent disciplines—security, product, operations—not just their primary stack.
• Make human skills a performance criterion. If collaboration and empathy appear nowhere in performance reviews or career ladders, they won’t be treated as serious professional goals.
• Recognize that culture problems are skills problems. Friction between development and operations, poor incident communication, and siloed decision-making are often symptoms of under-developed human skills, not just organizational design failures.

The Bottom Line

DevOps tooling and automation will continue to advance rapidly. But the data from the DevOps Institute’s 2020 report makes a compelling case that technology alone cannot carry a DevOps transformation. Organizations that neglect the human side—failing to upskill their people, undervaluing collaboration and empathy, and treating soft skills as peripheral—will struggle to see returns on their technical investments. The competitive advantage increasingly belongs to organizations that develop the whole engineer, not just the technical one.

Overview

Xebia Academy Global, the education business unit of Xebia, has entered into a strategic partnership with DevOps Institute to accelerate DevOps adoption worldwide through digital learning and workforce upskilling. The partnership makes accredited DevOps certifications and learning materials available to students, individual professionals, and corporate groups seeking to develop practical skills in modern DevOps practices.

What the Partnership Covers

Under this collaboration, Xebia Academy Global gains access to DevOps Institute’s portfolio of accredited courses and certification programs. The curriculum spans a broad range of disciplines relevant to enterprise IT and modern software delivery, including:

• DevOps fundamentals and advanced practices
• Agile methodologies
• Security integration (DevSecOps)
• Testing practices
• Continuous Delivery (CD)
• Site Reliability Engineering (SRE)

Each course is designed to prepare IT professionals for DevOps Institute certification exams, which support career advancement and signal competency in recognized DevOps practices.

The DevOps Institute SKIL Framework

DevOps Institute structures its global education partner ecosystem around the SKIL Framework — an acronym for Skills, Knowledge, Ideas, and Learning. The framework is central to the institute’s mission of advancing what it calls the “Humans of DevOps”: the people, culture, and collaborative practices that underpin successful DevOps transformations, not just the tooling.

By becoming an accredited Global Education Partner, Xebia Academy Global is aligned with this framework, ensuring that its course delivery meets DevOps Institute quality standards and covers up-to-date patterns and practices for enterprise IT environments.

Why This Matters for IT Professionals and Enterprises

For individual IT professionals, recognized certifications serve as verifiable proof of competency in a competitive job market. DevOps roles continue to grow in demand, and structured learning paths — backed by globally recognized credentials — help candidates differentiate themselves and accelerate career progression.

For enterprises, the partnership offers a structured route to upskilling development, operations, and security teams at scale. Organizations looking to improve their software release velocity, reduce deployment risk, and foster cross-functional autonomy can leverage Xebia Academy Global’s delivery capabilities combined with DevOps Institute’s curriculum.

Quotes from Partnership Stakeholders

Brijesh Kohli, Director of Xebia Academy Global, commented on the announcement:

“We constantly strive to bring in courses that are relevant and futuristic. The present tie-up with DevOps Institute is one such effort, where we want our students and corporate workforce to be future ready and reinvent themselves. The synergy will help and equip them to adopt the latest advancements in the technology with the support of people who are the best in disseminating this knowledge.”

Brijesh Kohli, Director, Xebia Academy Global

Dheeraj Nayal, Global Community Ambassador at DevOps Institute Inc., added:

“DevOps Institute’s Education partners support the Skills, Knowledge, Ideas, and Learning (SKIL) Framework for advancing the ‘Humans of DevOps’ and millions of IT professionals around the world. DevOps Institute’s new Global Education Partner – Xebia Academy Global will optimize the accredited courses and programs that teach students and professionals about the most up-to-date patterns and practices for enterprise IT, DevOps, Agile, Security, Testing, Continuous Delivery and Site Reliability Engineering (SRE). Each course and program prepares IT professionals to pass DevOps Institute’s corresponding certification exams which help in career advancement and support emerging practices within the DevOps community.”

Dheeraj Nayal, Global Community Ambassador, DevOps Institute Inc.

About the Organizations

Xebia Academy Global is the education and training arm of Xebia, a technology consulting company. It delivers training programs in software development, DevOps, Agile, and cloud technologies to corporate and individual learners globally.

DevOps Institute is a global membership association focused on advancing the human elements of DevOps. It offers a range of professional certifications — including DevOps Foundation, SRE Foundation, and DevSecOps Engineering — that are widely recognized by hiring organizations and enterprises adopting DevOps at scale.

With more enterprises continuing to integrate FaaS capabilities into both edge and cloud, we’ve seen big changes in the developer community.

Why Continuous Learning Belongs in a CI/CD Toolchain

The DevOps toolchain expands constantly. New pipeline stages, security scanners, observability platforms, and cloud-native runtimes appear faster than traditional corporate training programmes can absorb them. By the time a scheduled training course runs, the tool version covered may already be obsolete.

One practical response is to embed just-in-time training materials directly inside the toolchain itself — short instructional videos, quick-reference guides, or contextual links that surface exactly when a developer or IT admin is about to use an unfamiliar feature. The goal is not to replace deep-dive courses, but to reduce friction at the moment of need and accelerate onboarding when toolsets evolve.

Three Integration Options

There are three realistic paths for integrating training content into a CI/CD pipeline: third-party training platforms, vendor-supplied resources, and in-house content. Each has different cost, maintenance, and customisation trade-offs.

1. Third-Party Training Platforms

Online training marketplaces — such as Pluralsight, LinkedIn Learning, Cloud Academy, and A Cloud Guru (which acquired Linux Academy) — offer broad catalogues that cover most DevOps tools and practices. The key integration steps are:

• Select the right platform. Evaluate catalogue depth, update frequency, and relevance to your specific stack before committing.
• Secure an enterprise licence. Individual memberships typically do not include API access. Negotiate an enterprise or team plan that explicitly permits toolchain integration, and read the licence terms carefully.
• Use the provider’s API. Most major platforms publish well-documented APIs under their enterprise tiers. These allow you to surface course links, track completion, or embed video players directly in a developer portal or pipeline dashboard.
• Involve developers from the start. Whether integration is handled in-house or by a third-party services firm that built the toolchain, developers must be included from content selection through testing. Their input determines what actually gets used.

2. Vendor-Supplied Training Resources

Many CI/CD and DevOps tool vendors publish their own onboarding content — webinars, walkthroughs, and product documentation — directly on their websites. This content is often free, authoritative, and kept in sync with product releases.

The main challenge is integration. Vendor content lives on external sites and may be behind a login wall. Before linking to it from your toolchain, plan:

• Whether the content requires authentication and how that affects the user experience inside your pipeline tools.
• How to handle URL changes or content reorganisations on the vendor’s side — broken links erode trust quickly.
• Whether you can deep-link to specific articles or videos, or only to a generic documentation landing page.

3. In-House Training Content

Enterprises with proprietary workflows, internal tooling, or compliance requirements often need training content that no external vendor can supply. Several approaches are available:

• Learning Management System (LMS) integration. Most enterprise LMS platforms expose REST APIs that allow a CI/CD pipeline or developer portal to surface relevant course links, check completion status, or launch content in context.
• Confluence workspaces. Atlassian Confluence is already part of many DevOps toolchains. You can create a dedicated training workspace, publish written guides and embed videos using the Widget Connector macro, and link directly from Jira tickets, pipeline dashboards, or README files. Confluence’s space blueprints provide ready-made templates for knowledge bases and documentation pages that teams can customise. Team members with appropriate permissions can also comment on or edit content collaboratively.
• Team wikis. If Atlassian tools are not part of the stack, alternatives such as the built-in wiki in Microsoft Teams serve a similar purpose for publishing and linking to internal guides.

Maintaining Training Content Over Time

Embedding training into a toolchain creates an ongoing maintenance responsibility. Outdated content is worse than no content — it wastes time and signals to engineers that they can safely ignore the training section entirely.

API and Integration Maintenance

Third-party platform integrations depend on stable APIs. When a provider releases a breaking API change, or is acquired and migrates to a new platform, the integration breaks. Assign ownership of these integrations and review them on a regular cadence — at minimum, whenever your pipeline tooling or the training provider undergoes a major update.

Content Currency

In-house content requires a clear ownership model. Options include:

• Assigning content maintenance to a specific DevOps team member as a recurring responsibility.
• Engaging a dedicated technical writer who reviews and updates materials on a scheduled basis.
• Tagging content with a last reviewed date so readers can judge freshness themselves.

Gathering Feedback

One of the foundational principles of DevOps is rapid, continuous feedback. Apply the same principle to the training content itself. Solicit structured feedback from engineers after they use a training resource — a simple thumbs-up/thumbs-down widget, a Slack reaction, or a brief survey. Use that signal to prioritise updates, retire stale content, and commission new material where gaps appear.

Key Takeaways

• Just-in-time training embedded in a CI/CD toolchain reduces onboarding friction when tools or workflows change.
• Enterprise licences with API access are a prerequisite for integrating third-party platforms; individual memberships are not sufficient.
• Vendor-supplied documentation is authoritative but requires a link-management strategy to handle external content changes.
• Confluence, LMS APIs, and team wikis are practical vehicles for in-house training content.
• Outdated training content actively harms adoption; assign clear ownership and review schedules from day one.
• Apply DevOps feedback loops to the training programme itself — measure engagement and iterate.

The Kubernetes Question Everyone Is Afraid to Ask

Kubernetes has become the default answer to almost every container orchestration question. But is it the right answer for your organisation? As developer and commentator Paul Johnston asked publicly, “Why Kubernetes?” — and the volume and passion of the responses revealed that this is a question many practitioners are quietly asking themselves.

Before committing to the operational overhead of running Kubernetes, it is worth examining the most common justifications critically. The two most cited reasons are avoiding cloud vendor lock-in and following industry best practice. Neither holds up to scrutiny as cleanly as the sales pitch suggests.

The Lock-In Myth

Cloud portability is probably the single most frequently cited reason for adopting Kubernetes. The argument goes: if your workloads run on Kubernetes, you can move them between AWS, GCP, Azure, or on-premises without significant rework. In theory, this is sound. In practice, it is complicated.

• Managed services create their own lock-in. Most production workloads depend on managed databases, message queues, identity providers, and monitoring stacks — all of which are cloud-specific. Kubernetes does not abstract those dependencies away.
• “Portable” clusters are not free. As Neal Gompa notes, bare Kubernetes does not give you cloud portability out of the box. You need additional tooling, disciplined API usage, and often significant architectural compromises to achieve meaningful portability.
• Teams rarely actually move. Johnston observed that the same engineers who cited multi-cloud portability as their primary motivation also admitted they had no concrete plans to switch providers. The exit strategy remains theoretical.

Analyst Lawrence Hecht makes a fair counter-point: “Fear of lock-in is rational. It is rational to want to have an exit strategy even if you don’t plan to use it.” That is a reasonable position. But an exit strategy does not have to mean Kubernetes. Well-containerised applications running on plain Docker, or even well-structured monoliths on virtual machines, can offer meaningful portability without the operational overhead Kubernetes introduces.

“An attempt to evade lock-in should not automatically mean Kubernetes. We had a decent amount of portability with monoliths sitting on virtual servers. I’d argue we have less portability with Kubernetes now.”

— Paul Johnston

The Complexity Cost

Kubernetes is a sophisticated distributed systems platform. It solves genuinely hard problems around scheduling, self-healing, scaling, and service discovery. But it introduces a substantial operational burden in return:

• Cluster provisioning and lifecycle management
• Networking (CNI plugins, ingress controllers, service meshes)
• Storage provisioning and persistent volume management
• RBAC, secrets management, and security policy
• Observability: metrics, logging, and tracing pipelines
• Upgrade paths across control plane and node pools

For teams without dedicated platform engineering expertise, this overhead can consume more capacity than it saves. As James Thomason argues, Kubernetes may be overkill for the vast majority of production workloads. Developers who see it as a way to “run like Google” may be adopting the complexity without the scale that justifies it.

Johnston notes that many CTOs adopt Kubernetes because they feel they have to — either inheriting it from a previous team or viewing it as the next big industry standard — and then regret it. The regret tends to arrive when the complexity of operating Kubernetes outweighs the problems it was supposed to solve.

When Kubernetes Does Make Sense

None of this means Kubernetes is without merit. There are clear scenarios where it is the right choice:

• Large microservices architectures. When you are running dozens or hundreds of independently deployable services, the scheduling, service discovery, and health management capabilities of Kubernetes provide real value.
• Teams with platform engineering investment. Organisations that can staff and maintain a platform team get a consistent, programmable infrastructure layer that scales well.
• Infrastructure abstraction across environments. As Joseph Mente argues, Kubernetes provides a useful layer of abstraction that helps when moving between services or environments, even if it does not fully eliminate lock-in.
• Transferable developer skills. As Don Syme points out, engineers who learn Kubernetes build skills that are broadly valued across employers and industries, regardless of which cloud those employers use.

The Future: Kubernetes as Infrastructure, Not Application Concern

There is a compelling argument, made by James Urquhart among others, that the current phase of Kubernetes ubiquity is transitional. Kubernetes will likely “win” the container orchestration space — but not by requiring every developer to understand its internals. Instead, it will disappear into the abstractions above it.

This is already happening. Managed Kubernetes services (EKS, GKE, AKS) hide much of the control plane complexity. Serverless platforms like Google Cloud Run and AWS App Runner run containers on Kubernetes infrastructure without exposing Kubernetes APIs to developers. Platforms like Heroku-style PaaS tools and internal developer platforms built on top of Kubernetes abstract it further still.

The practical implication: the question is shifting from “should we use Kubernetes?” to “at which layer should our teams interact with it?” For many organisations, the right answer is that application developers should not interact with it directly at all.

A Decision Framework: Do You Actually Need Kubernetes?

Before committing, ask your team these questions honestly:

1. What specific problem are you solving? If the answer is “portability” or “it’s the industry standard”, dig deeper. Those are not engineering requirements.
2. Do you have the operational capacity? Running Kubernetes well requires sustained investment. Do you have — or can you hire — people with the skills to do it?
3. Would a managed platform solve it? For many teams, a managed container service, a PaaS, or even well-structured Docker Compose deployments on VMs will cover 90% of needs at a fraction of the complexity.
4. What does your scale actually look like? Kubernetes is designed for scale. If you have five services and one team, the overhead-to-benefit ratio is probably not in your favour.
5. Is the portability benefit real? Map out your actual external dependencies. If you are using five managed cloud services, Kubernetes on the compute layer will not make you portable.

Conclusion

Kubernetes is a powerful platform that solves real problems at scale. It is also frequently adopted for the wrong reasons — fashion, fear, or inherited decisions — and imposes real complexity costs that teams underestimate. Cloud portability, the most common justification, is real but limited: Kubernetes alone does not free you from vendor lock-in when your application depends on cloud-specific managed services.

The honest answer to “Do you need Kubernetes?” is: it depends on your scale, your team’s capacity, and whether you have a concrete engineering problem that Kubernetes solves better than simpler alternatives. If you cannot answer those questions clearly, that is a signal to slow down before you board the bandwagon.

DevOps: A Rapidly Maturing Discipline

The introduction of DevOps fundamentally changed how development and operations teams work together. Practices like continuous integration (CI) and continuous delivery (CD) moved from niche concepts to industry standards, and organisations that adopted them saw measurable improvements in deployment frequency, lead time, and system stability.

As DevOps matured, several consistent benefits became clear across adopting organisations:

• Auditability and repeatability: Infrastructure as code and pipeline-driven deployments mean every change is logged, versioned, and reproducible.
• Low cost of change: Automated testing and short feedback loops reduce the risk and expense of making changes.
• Cross-functional collaboration: Breaking down silos between development, operations, and QA teams accelerates delivery.
• Speed at scale: Automation enables teams to ship features and fixes faster without sacrificing quality.

At the same time, the cloud ecosystem evolved in parallel, providing developers with richer, more intelligent tooling — from managed Kubernetes services to serverless platforms — that reinforces DevOps discipline and increases operational efficiency.

The Emergence of DevSecOps

One significant gap in early DevOps adoption was security. The pressure to ship quickly often meant that security checks were treated as a final gate rather than an integral part of the pipeline. Retrofitting security after a build is both costly and error-prone — vulnerabilities that could have been caught at the design or coding stage become much more expensive to remediate post-deployment.

DevSecOps addresses this directly by embedding security practices throughout the entire software development lifecycle (SDLC). Rather than a separate security review at the end, security becomes a shared responsibility starting from the first line of code.

Two Pillars of a Successful DevSecOps Adoption

1. Collaboration Across Disciplines

Historically, developers focused on logic, algorithms, and feature delivery, with security treated as someone else’s concern. DevSecOps requires a cultural shift: security engineers must be involved from the architecture stage, and developers must be equipped with the knowledge to write secure code. This means:

• Including security experts in design reviews and sprint planning.
• Training developers to recognise common vulnerability patterns (e.g., OWASP Top 10).
• Establishing shared ownership of security outcomes across development, operations, and security teams.

2. Continuous Review and Monitoring

A key principle of DevSecOps is that security review is not a one-time event — it is a continuous process. Practical implementation includes:

• Static application security testing (SAST) integrated into CI pipelines to catch vulnerable code before it merges.
• Dynamic application security testing (DAST) run against staging environments.
• Compliance monitoring for frameworks such as PCI-DSS and GDPR, enforced programmatically rather than manually.
• Defined incident response playbooks so teams know exactly how to react when a threat is detected.
• Security gate criteria applied at each stage: from developer commit, through build, to test and production deployment.

The goal is that security posture improves continuously rather than degrading between infrequent audits.

What Comes After DevSecOps? Two Emerging Theories

DevSecOps represents one evolution of the DevOps model, but it is unlikely to be the last. Two distinct theories about the future of Ops practices have gained traction in the engineering community.

Theory 1: NoOps — Full Automation of Operations

The NoOps concept takes automation to its logical extreme. In a mature NoOps environment, the software provisioning pipeline is so thoroughly automated that human intervention for routine operational tasks is largely eliminated. Key characteristics of a NoOps vision include:

• Code standards, security policies, library compliance, and regulatory requirements are enforced automatically at build time.
• Infrastructure provisioning, scaling, and remediation are handled by the platform with no manual input required.
• Engineers shift from performing operations to monitoring automated systems and investigating anomalies.

In practice, NoOps is more of a spectrum than a binary state. Fully serverless architectures and managed platform services (such as fully managed databases, API gateways, and container orchestration services) move teams significantly along this spectrum. The potential upside is a higher, more consistent baseline of security and reliability — because every deployment automatically meets a defined standard.

Theory 2: MLOps — Machine Learning Meets DevOps

The second theory is that DevOps will not disappear but will evolve into specialised variants, with MLOps being the most prominent example. MLOps applies DevOps principles — automation, versioning, CI/CD, monitoring — to the machine learning lifecycle.

Where traditional DevOps manages the lifecycle of application code, MLOps manages the lifecycle of data, models, and experiments alongside application code. Core MLOps capabilities include:

• Repeatable workflows: Data pipelines and model training runs are versioned and reproducible, so any model can be retrained from scratch with the same result.
• Automated model validation: Quality and performance gates are applied to models before they are deployed to production — analogous to unit and integration tests for code.
• Model monitoring: Production models are monitored for data drift, concept drift, and performance degradation, triggering automated retraining pipelines when needed.
• Regulatory compliance: Because model training runs are reproducible and auditable, organisations can demonstrate to regulators exactly how a model was built and what data it was trained on.

Like DevSecOps, effective MLOps requires breaking down organisational silos. Data scientists, ML engineers, software developers, and platform engineers must collaborate closely for MLOps to deliver value. Isolated data science work that produces notebooks but not deployable, monitored models is the MLOps equivalent of security bolted on at the end of a DevOps pipeline.

The Common Thread: Collaboration and Shifting Left

Whether the discussion is DevSecOps, NoOps, or MLOps, a consistent theme emerges: the most effective evolution of DevOps is one that moves key concerns — security, compliance, quality, reproducibility — earlier in the development lifecycle rather than addressing them at the end. This “shift left” philosophy reduces rework, lowers the cost of defects, and produces more resilient systems.

Equally consistent is the requirement for genuine cross-functional collaboration. No Ops evolution succeeds as a purely technical exercise. It requires teams with different expertise — development, operations, security, data science — to share ownership of outcomes rather than handing work over a wall.

Looking Ahead

DevOps is not a finished idea. The arrival of DevSecOps demonstrates that the model can absorb new disciplines and improve as a result. MLOps is a natural next step as machine learning becomes a standard component of production software systems rather than a specialist research activity.

For engineering teams planning their tooling and process roadmap, the practical implications are clear:

• Treat security, compliance, and model governance as engineering problems to be automated, not manual checkpoints.
• Invest in platform capabilities that reduce toil and push operational concerns toward automated enforcement.
• Build cross-functional teams and cultures rather than passing artefacts between siloed groups.
• Adopt MLOps practices early if machine learning is part of your product roadmap — retrofitting operational discipline onto models in production is as painful as retrofitting security onto deployed code.

The evolution of DevOps is ongoing. Teams that engage with it proactively — rather than waiting for practices to fully mature before adopting them — will be best positioned to deliver secure, reliable, and intelligent software systems.