As you know that over the months, what drastic changes are occurring in the field of IT software infrastructure and with which technologically advances. If you have worked with certain sorts of Splunk management tools. After using it, you will adopt a grip like a pro user or advanced user. You know the real facts and figures about the daily delivery of software toolsets with great advancement. How much they’re serving and co-operative with machine-learning data. If you want hands-on phenomenal structured data on a single desk or a single screen. You know the real condition of Splunk like how it works and how it works for the whole IT software management. Doing monitoring, searching, indexing, visualizing, and analyzing from the web server application. They managing the real machine-generated data like “Data for everything values“. Easily you can get analytic generated data with the configuring system.

 If you have gotten all your necessary equipment with lots of source data related to the Splunk user. Let’s talk about the Splunk consultant. The thorough ideology about the consultant gives you all scenario-based research knowledge with an in-depth understanding about deployment methodology and how can you implement on large installations and has deep ability on an expertise-level of Splunk architectures with clustering and scalability data topic. They make you an expert at a certain level of acquiring and managing the data-driven industry. If you meet with it then they create unexpected belief in the work flowing concept of learning and generate monarchy level strength with effective support. So, get every method and gigantic database management control with an advanced learning program at a single mind. If you get introduced to a core consultant then you can acquire certain limits of working experiences with skill practices. So, makes your life busy with lots of server data and data for living conditions.

What are the benefits of Splunk Certified Consultant?

If you get correct brief concepts on how the spunk core consultant makes your life better with lots of equipped information in advance with great job support. Gives the stack learning on Splunk admin, security system, architecture, and phantom. Consultants possess the ability to implementation of all software programs with lots of data performances. With lots of several benefits as much as possible:

• Tailor training to your use cases, workflows and data
• Evaluate workflows
• Don’t look a product in vacuum and evaluate the overall process that they integrate with
• Quantify the value of splunk
• Repurpose your data
• Utilize new ( or little known )features in Splunk
• Prevent data loss

Why we should learn Splunk Certified Consultant?

If you’re looking for the right machine-learning data. So, let’s come to the point of how much it is good and what is the fact & reason behind the becoming of Splunk certified consultant. Mostly, the upcoming technologies data issues are going to engage in the field of multi-tier IT organizations. The learning process about the consultant comes after how much you have self-paced into the machine-generated data. Like you perform on the basis of what you have learnt about Splunk core, admin, security management, and developer. After getting the hands-on challenge ideology, you can act as a core qualified consultant. Then, what is the need for learning? I have few queries in that you can generate a mind-mapping concept on that as insufficient understanding of Splunk architecture can undermine performance, stability, and availability or user may still not understand how to apply the training to their data/ environment. This makes you experts on the basis of what you’re acquiring and implementing in relation to the unstructured large amounts of data. You can easily get command over all the possibilities scenario. On what is data collectively, crawling reports, datasets, and highly configurable alerts with real learning dashboard. This course will produce awareness about the whole IT industry or firm. This sort of highly comprehensive ideology will make eligible to you for any kind of job support role.

How our Splunk Certified Consultant course/training would help?

This training program or courses will make your expertise and develop a highly dense experience about to install the software, how to manage them with great data support, how the Splunk architecture works with multi-tier projects, due to working benefits of a small and large industry of firm. They indulge with lots of working and practical knowledge with some basic concepts of how to monitoring the unscripted data loads, searching for machine-generated data with unlimited sources, indexing the forwarder-based data, and analyzing the real-time data with continuous visibility. This provides you lots of ideas like the installation process, how much implementation in your software system, effectively working on the various dashboard form. How much it is easier to get into your business needs. And how can you ingest software in your entities for real machine learning data? So, if the industries have happened data for everything. This will really help you in the team working process and effectively build your business need with great support.

After getting the working experience on the core consultant tutorial, let’s come to the main topic on where seeks after learning and experiences. After completing any courses and tutorials, we move to the part of certification. It proves how much you’re getting from the course agenda or configuring skillsets. This makes your life appropriate with lots of work flowing ideas and give mental stability with great backhand support. This plays an effective role in career paths like Splunk engineer, spunk architecture, and Splunk administration engineer, etc. If you create high-impact skillful knowledge then you can compete with any IT entities or industry. Regarding this, you will get a high-paid average salary with lots of job demands and opportunities. One more thing is that certification demonstrates the consultant’s ability how to install, implementation of Splunk environment, resize the capacity of software, and maximize the values according to their needs.

Related video:

What is Splunk Phantom Admin?

If you work on a security team, you probably deal with complex security infrastructure and a range of technologies from multiple vendors. You’re likely exposed to unquantified risk stemming from a constantly expanding attack surface and escalating volumes of threats. And like everyone else, you have limited resources to defend your entities. There’s is never enough budget or people. Fortunately, there is a better way. Force multiply your efforts with a Splunk phantom security orchestration, automation, and response platform.

Phantom integrates your team, processes, and tools together so you can bring your best defense forward. It allows you to automate repetitive tasks. So, you can focus on other mission-critical issues. It helps you up-level how you manage events and cases and improve collaboration and training across the team. Phantom allows you to triage every by executing routine workflows in seconds or minutes instead of hours or more if done manually. It helps you make your SOC operations repeatable and auditable. Teams are able to manage events and cases the same way, every time, regardless of the person working on them. You’re able to accurately measure and reduce risk. And finally, you can demonstrate measurable impacts on your key performance metrics like MTTR. See what phantom can do for you. Get started now with the phantom community edition.    

What are the benefits of Splunk Phantom Admin?

From the beginning, we have learned many sorts of Splunk platforms series like Splunk core developer, enterprise security, cloud admin, and IT service intelligence. This time we are meeting with Splunk phantom. Every software has its own performance, duties, and responsibilities with great impact then query raises that what about the software defensive security and how the software maintains their accuracy with higher scalability. With this overview, there are certain benefits related to Splunk phantom certified admin such as: 

• Create security for the management system
• Highly integration process
• Security work flowing system
• Automation security system
• Advanced threat catching actions

Why we should learn Splunk Phantom Admin?

Nowadays, this trending IT organization with the higher escalating tools. We are working with different technologies at a scale of interactions. If we are using the software too much then we need threat detection analysts and security system services. Then, a query comes as that why we should learn Splunk phantom and become certified. The most reason is that to automate and accelerate the process like shortage of IT security professionals, a large number of security point solutions, challenges of unstructured data, the investigative approach, people & processes, and workflow of threat mitigation timeline. And the admin facing common security operations challenges such as resources, products, alerts, static, speed, and costs. So, if you move along with Splunk phantom then you can evaluate delivering quality intelligence, improving the efficiency and efficacy of operations, enhancing incident response, and improves reporting and knowledge capture. As a Splunk phantom certified admin, you can make your software more planning, designing, creating, and debugging playbooks for phantom.

How our Splunk Phantom Admin course/training would help?

These tutorial courses will help you with the learning process of Splunk phantom admin with the SOAR platform (security orchestration, automation & response). This will make you more configure about the software system security. It solves security operations problems. Suppose, your software have limited resources, too many alerts, too many siloed products, lack of process, speed, & cost then it remediates with force multiply your team, clear 80% of alerts with no human interaction, unlock value from previous investments, establish SOPs (standard operating procedures) to be more effective, from 30 minutes to 40 seconds, & definitive ROI and hard cost savings. And it will give you work flowing ideology on security data, playbooks, actions, apps, assets, and owners.

After, getting the processed values then comes to the certification. If you get certified with the completion of training then it gives you competitive strength in automation and orchestration first, community focus, and enterprise architecture. This will make you more efficient in IT software infrastructure for ideal job opportunities with an expected high-paid salary. I wish that these statistics will give you more understanding of futuristic points of view.

Related video:

Why Government and DevOps Are a Natural Fit

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

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

Shift-Left Security in Practice

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

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

Cross-Functional Collaboration Without Turf Wars

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

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

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

Regulatory Alignment as an Unlikely DevOps Accelerator

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

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

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

Speed as a Security Tool

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

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

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

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

Actionable Recommendations for Private-Sector Teams

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

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

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

Key Takeaway

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

Overview: A Beat-and-Decline Earnings Event

Datadog (NASDAQ: DDOG) reported its Q2 2020 earnings on August 6th, delivering a revenue beat and raising its full-year guidance. Despite those positives, the stock dropped approximately 16% in after-hours trading — a counter-intuitive reaction that left many investors searching for an explanation.

The most likely culprits were conservative forward guidance and management commentary about large customers pulling back on cloud infrastructure spending — a macro headwind that directly affects Datadog’s consumption-based revenue model.

Q2 2020 Customer and Revenue Metrics

The underlying business metrics for Q2 2020 remained strong, even if growth rates moderated slightly from the prior quarter:

• Total customers: Approximately 12,100, representing 37% year-over-year growth (down from 40% YoY in Q1).
• Large customers (ARR ≥ $100,000): 1,015, up 71% YoY (down from 89% YoY in Q1).
• Large customer additions by quarter: 131 in Q4 2019, 102 in Q1 2020, and 45 in Q2 2020 — a declining sequential add rate.
• Q2 revenue: $140 million, following $131 million in Q1 2020.
• Gross margin: Approximately 80%, an increase of roughly 5 percentage points year-over-year.

The slowdown in large customer additions is notable because customers with ARR of $100,000 or more account for approximately 75% of Datadog’s total revenue. Management acknowledged that some of these enterprise customers were actively reducing cloud infrastructure consumption as a cost-cutting measure during the economic uncertainty of mid-2020, which rippled directly into Datadog’s usage-based billing.

Why Large Customer Spending Matters So Much

Datadog’s pricing model is closely tied to the volume of infrastructure being monitored. When a large enterprise reduces its cloud footprint — whether by shutting down non-essential workloads or consolidating environments — Datadog’s billable usage shrinks proportionally. This is a structural characteristic of usage-based SaaS businesses that can cause revenue growth to decelerate even when the customer count is growing.

The silver lining, as noted in the original analysis, is that a significant portion of large enterprises’ IT infrastructure still runs on-premises. As economic conditions improve and cloud migration accelerates, these customers represent an organic expansion opportunity without Datadog needing to win new logos.

Full-Year 2020 Guidance

Management raised its full-year 2020 revenue outlook to between $566 million and $572 million. Working backward from the Q1 and Q2 actuals:

• Q1 actual: $131 million
• Q2 actual: $140 million
• H2 implied guidance: approximately $295–$301 million combined
• Q3 guidance implied: approximately $144 million (~50% YoY growth)
• Q4 guidance implied: approximately $151–$157 million (~35% YoY growth)

The implied Q4 deceleration to roughly 35% YoY was seen as particularly conservative, given that Q4 had historically been Datadog’s strongest quarter. Analysts and investors familiar with Datadog’s track record of under-promising and over-delivering took this as a signal that actual results could come in meaningfully higher.

Product Expansion and Cross-Selling Momentum

One of the more compelling data points from Q2 was the continued adoption of multiple Datadog products within the same customer account:

• Two or more products: 68% of customers (up from 63% in Q1 and 40% one year prior).
• Four or more products: 15% of customers.

This multi-product adoption rate is a strong indicator of platform stickiness. Customers deeply integrated across observability, APM, log management, security, and related modules are far less likely to churn and are natural candidates for upsell as they expand their infrastructure. Cross-selling across the platform was identified as a key potential catalyst for beating the conservative full-year guidance.

Datadog also received industry recognition during the quarter, including the 2020 Gartner Peer Insights Customers’ Choice designation for IT Infrastructure Monitoring Tools and the “Best Mission-Based Data Solution” award as part of the 2020 AWS Public Sector Partners Awards.

Convertible Note Offering and the Undefined Labs Acquisition

On June 2, 2020, Datadog completed a private offering of $747.5 million in aggregate principal of 0.125% Convertible Senior Notes due in 2026. The initial conversion price was approximately $92.30 per share, representing a roughly 37.5% premium over the stock price at the time of issuance.

A portion of those proceeds funded the acquisition of Undefined Labs, a startup focused on observability tooling for developer and test workflows. Undefined Labs was founded in 2018 and had offices in San Francisco and Madrid. The acquisition price was not publicly disclosed. The deal signals Datadog’s intent to extend its platform further left into the software development lifecycle — moving from purely operational observability toward developer-facing tooling.

Valuation Context After the Sell-Off

Following the 16% after-hours drop, Datadog’s price-to-sales (P/S) ratio fell to approximately 39x at the time of writing. While still elevated in absolute terms, this was more defensible when compared to the company’s revenue growth rate relative to peers. The original analysis cited the following approximate P/S ratios for competitors as of that period:

• Elastic (ESTC): ~16x P/S
• Splunk (SPLK): ~13x P/S
• New Relic (NEWR): ~5x P/S

Datadog’s premium valuation has historically been justified by its significantly higher revenue growth rate compared to these competitors. New Relic, in particular, was noted as being in the midst of a major platform restructuring effort to modernize its product and close the competitive gap with Datadog.

Investment Takeaway from August 2020

The core argument in the original analysis was that the post-earnings sell-off was an overreaction driven by conservative guidance and macro noise rather than deteriorating fundamentals. Key points of the bull case at the time:

• Management has a documented history of setting conservative guidance and then beating it.
• The multi-product cross-selling story remained intact and was accelerating.
• Large customer cloud spending headwinds are cyclical and likely to reverse as economic conditions normalize.
• Gross margins of ~80% demonstrate strong unit economics.
• The Undefined Labs acquisition reinforced the company’s commitment to broadening the platform.

The suggested approach for long-term investors was dollar-cost averaging, with the mid-$70s price level identified as a reasonable entry point to accumulate additional shares.

Note: All financial figures, stock prices, and valuation multiples in this article reflect conditions as of August 2020. This article is for informational purposes only and does not constitute investment advice. Original analysis sourced from Seeking Alpha.

Kubernetes: The Emerging Standard for Agile IT

After many years of cloud-native platforms competing for industry acceptance, Kubernetes has moved from a promising technology to the de facto standard for rapidly developing, testing, and deploying applications. Its architecture is increasingly seen as the foundation upon which modern, agile IT organisations are built.

The IT outcomes are measurable: a wide majority of businesses that adopt Kubernetes report clear benefits, including more efficient resource utilisation and shorter software development cycles. But the advantages extend far beyond the engineering team. In an era where technology capability directly determines competitive position and growth trajectory, a more agile IT core means a more agile business overall.

The Business Case for Kubernetes

Empowering development teams to do more in less time delivers obvious value on paper. However, because Kubernetes carries inherent architectural complexity, the way these benefits actually materialise can be difficult to articulate to stakeholders outside IT—especially during the early stages of adoption.

The following sections break down where Kubernetes delivers tangible, cross-functional business value, and why overcoming its initial learning curve is worthwhile.

A Genuine Sense of Operational Agility

Kubernetes fundamentally shifts the focus from managing infrastructure to building applications. Rather than waiting weeks or months for infrastructure provisioning, development teams can use a self-service model to get the compute resources they need in days or even hours.

This matters because the ability to react quickly to changing customer behaviour is critical in any competitive market. Working in smaller batch processes—a natural fit with Kubernetes-based workflows—lets product and customer engagement teams sense and respond to market signals much faster than traditional release cycles allow.

• Shorter release cycles: Teams ship smaller, more frequent updates rather than large, infrequent releases.
• Faster feedback loops: Quicker deployments mean quicker learning from real user behaviour.
• Reduced time-to-market: New features and products reach customers faster, ahead of slower competitors.

By using Kubernetes to make delivery pipelines more agile, leadership teams gain the ability to sustainably explore new business opportunities, experiment with monetisation strategies, and evolve their technology offering as the market shifts.

Accelerating Profitability Through Efficiency

During periods of economic uncertainty, cost efficiency becomes as important as speed. Kubernetes addresses both simultaneously.

Kubernetes eliminates many of the smaller operational friction points that typically slow application development. Its configuration modules, declarative deployment model, and built-in automation capabilities speed up iteration significantly. In practical terms, this means products can be built, tested, incrementally improved, and shipped to market faster than was possible with traditional infrastructure approaches.

Because so much of the operational overhead is automated and standardised, engineering resources can be redirected toward innovation rather than maintenance. This cost-effective reallocation of effort is particularly valuable when budgets are under pressure and organisations need to extract maximum value from existing headcount.

Competitors that have not adopted container orchestration are likely to find their software supply chains remain a bottleneck—limiting how quickly they can evolve their products and respond to market change.

Sustainable, Flexible Paths to Growth

Kubernetes is a multi-cloud platform by design. Organisations can run Kubernetes clusters on whichever cloud provider—or combination of providers—best meets their cost, compliance, and performance requirements. This flexibility avoids vendor lock-in and allows workloads to be moved or distributed as business needs evolve.

Scalability is another key advantage. Applications can grow rapidly without requiring teams to re-architect infrastructure from scratch for each new project. Kubernetes handles the scheduling and scaling logic, freeing engineers to focus on application code rather than capacity planning.

Kubernetes also extends beyond centralised cloud environments. It can be deployed at the edge—in retail stores, manufacturing facilities, branch offices, or even aircraft—enabling software to run locally where low latency or intermittent connectivity makes a purely cloud-based model impractical.

Resilience and Security as a Business Outcome

The ability to allocate time and resources flexibly across product development also creates room for teams to do more thorough testing, gather broader user feedback, and address security vulnerabilities proactively rather than reactively. A Kubernetes-based workflow naturally supports practices such as continuous integration and continuous delivery (CI/CD), canary deployments, and automated rollback—all of which reduce the risk profile of releasing software.

As a result, organisations running Kubernetes are generally better prepared for disruption—whether that disruption comes in the form of a sudden spike in demand, a security incident, or broader macroeconomic instability—because their infrastructure and delivery processes are designed for adaptability from the outset.

Future-Proofing Your Infrastructure

The operational lessons of recent years have reinforced a simple reality: businesses cannot be over-prepared for the future. For infrastructure and platform engineering teams, this translates into a concrete mandate—build systems that can absorb unexpected load, pivot quickly, and scale reliably under pressure.

Kubernetes does not eliminate complexity, but it provides a standardised, extensible framework for managing that complexity at scale. Organisations that invest in building Kubernetes competency now are better positioned to weather future disruptions—and to move faster than their competitors when opportunities arise.

Key Takeaways

• Agility: Kubernetes enables self-service infrastructure provisioning, reducing wait times from weeks to hours and allowing teams to iterate faster.
• Cost efficiency: Automation and standardisation reduce operational overhead and free engineering resources for higher-value work.
• Multi-cloud flexibility: Run workloads on any cloud provider, or across multiple providers, without re-architecting applications.
• Edge support: Kubernetes can be deployed outside the data centre for use cases requiring local compute.
• Resilience: CI/CD pipelines, automated rollback, and declarative configuration reduce deployment risk and improve uptime.
• Future-proofing: Organisations with mature Kubernetes adoption are better equipped to handle unexpected demand, security threats, and market disruption.

ShareChat Completes Full Migration to Google Cloud

Google Cloud announced in June 2020 that ShareChat, an Indian social media platform focused on regional-language content, had fully migrated its infrastructure to Google Cloud. The migration covers the platform’s entire backend, moving away from its previous infrastructure setup to standardise on Google Cloud services.

No financial terms of the partnership were disclosed by either party.

Why the Migration Matters: Scale and User Base

ShareChat serves more than 60 million monthly active users across 15 different Indian languages. The platform is particularly popular in tier-II and tier-III cities, where a significant proportion of users connect over 2G mobile networks. This user profile creates a distinctive set of infrastructure challenges:

• High data volume: The platform processes large volumes of posts, likes, views, follower interactions, and media uploads continuously.
• Bandwidth constraints: Serving users on slower 2G connections requires optimised content delivery and minimal payload sizes to maintain a usable experience.
• Multi-language support: Handling 15 regional Indian languages adds complexity to content moderation, search, and recommendations infrastructure.
• Rapid growth: A fast-growing user base demands infrastructure that can scale horizontally without requiring re-architecture.

Key Engineering Goals Driving the Cloud Move

According to Venkatesh Ramaswamy, Vice-President of Engineering at ShareChat, the decision centred on three core engineering priorities:

• Reliability: Ensuring consistent uptime for a platform where users in underserved regions may have limited alternatives for connectivity.
• Scalability: Ability to handle traffic spikes driven by viral content without manual intervention.
• Observability: Gaining deeper visibility into system behaviour to diagnose performance issues faster.

“We made the decision to move and standardise on Google Cloud because we believe this will help us build better future technologies. The advances we have been able to drive with reliability, scalability and observability from day one have also been a major plus.”

— Venkatesh Ramaswamy, Vice-President (Engineering), ShareChat

Google Cloud’s Perspective on the Partnership

Karan Bajwa, Managing Director of Google Cloud India, stated that the partnership is designed to give ShareChat a scalable and reliable infrastructure foundation. The goal, according to Bajwa, is to enable ShareChat to continue delivering secure, cloud-native services to millions of users across India as the platform grows.

Infrastructure Implications for DevOps Teams

For DevOps and platform engineering practitioners, the ShareChat migration illustrates several common considerations when moving a high-traffic consumer platform to a public cloud provider:

• Full migration vs. hybrid: ShareChat opted for a complete migration rather than a hybrid approach, which simplifies operations but requires thorough pre-migration planning to avoid service disruption.
• Observability from day one: The explicit mention of observability as a key benefit suggests investment in tools such as distributed tracing, centralised logging, and metrics dashboards — areas where Google Cloud’s operations suite (formerly Stackdriver) is relevant.
• Edge performance for constrained networks: Minimising mobile bandwidth impact for 2G users points to use of CDN caching, adaptive bitrate media delivery, and efficient API design — all of which benefit from a cloud provider’s global network infrastructure.
• Cost efficiency at scale: Cloud migration at ShareChat’s scale is often justified in part by shifting from fixed capital expenditure on physical hardware to variable operational expenditure, with the ability to right-size resources based on actual demand.

Context: India’s Regional-Language Internet Growth

ShareChat’s trajectory reflects a broader trend in India’s internet market, where user growth has increasingly come from non-English speakers in smaller cities and rural areas. Platforms targeting this demographic face unique technical constraints — particularly around low-bandwidth delivery, local-language NLP for content understanding, and cost-effective infrastructure to serve price-sensitive markets. Cloud providers with strong presences in the Asia-Pacific region offer relevant advantages in latency, compliance, and partner ecosystem support for these use cases.

What Is Continuous Testing?

Continuous testing involves running automated tests at every stage of the software delivery pipeline to provide immediate feedback on the business risk associated with a software release. Unlike traditional test phases that occur late in the development cycle, continuous testing integrates quality checks from the earliest commit through production deployment.

Key characteristics of continuous testing include:

• Automated test execution triggered by code commits or pipeline events
• Risk-based test selection to keep feedback loops fast
• Integration with CI/CD tools such as Jenkins, GitLab CI, and Azure DevOps
• Reporting that maps test outcomes to business risk, not just pass/fail counts

Market Growth Drivers

Several converging forces are pushing enterprise adoption of continuous testing platforms and services:

Digital Transformation Initiatives

Enterprises undergoing digital transformation face pressure to ship software faster without sacrificing quality. Continuous testing provides the safety net that makes aggressive release cadences viable, directly linking it to broader digital transformation budgets.

Complex IT Infrastructure

Modern applications span microservices, containers, APIs, and multi-cloud environments. Testing this complexity manually is impractical. Automated continuous testing frameworks are well-suited to validate distributed systems at scale.

IoT Expansion

The proliferation of IoT-connected devices introduces new testing requirements around firmware, connectivity, security, and interoperability. Continuous testing vendors are expanding tooling to address these scenarios, opening new customer segments.

Demand for Faster Time-to-Market

Competitive markets reward speed. Continuous testing removes the bottleneck of manual regression cycles, enabling teams to release on-demand rather than on a fixed schedule.

Market Restraints

Despite strong tailwinds, the continuous testing market faces headwinds that slow adoption in some segments:

• Reliance on legacy and manual testing processes: Many organizations, particularly in regulated industries, maintain deeply embedded manual testing workflows that are difficult to modernize quickly.
• Skill gaps: Building and maintaining automated test suites at scale requires dedicated expertise in test automation frameworks, scripting, and CI/CD tooling.
• Test data management complexity: Continuous testing pipelines require reliable, compliant test data, which remains a significant operational challenge.

Key Vendors in the Continuous Testing Market

The global continuous testing market includes a mix of established IT services firms and specialist testing vendors. Notable players include:

• CA Technologies (now part of Broadcom)
• Cigniti Technologies Ltd
• EPAM Systems Inc.
• HCL Technologies Ltd
• IBM Corporation
• Mindtree Ltd
• QualiTest Ltd
• Syntel Inc. (now part of Atos)
• Tech Mahindra Limited
• Tricentis

Tricentis is widely recognized as a specialist continuous testing platform, while IBM, HCL, and Tech Mahindra approach the market primarily through managed and professional testing services. Each vendor targets different segments of the market in terms of deployment preference and organization size.

Market Segmentation

The continuous testing market can be analyzed across several dimensions:

By Deployment Model

• Public Cloud: SaaS-delivered testing platforms with minimal infrastructure overhead, preferred by SMEs and cloud-first organizations.
• Private Cloud: On-premises or dedicated cloud deployments favored by enterprises with strict data sovereignty or compliance requirements.
• Hybrid Cloud: A combination approach that lets organizations run sensitive test workloads internally while leveraging cloud elasticity for scale testing.

By Service Type

• Managed Services: Vendors operate and maintain the testing infrastructure and pipeline on behalf of the customer.
• Professional Services: Consulting, implementation, and training engagements to help organizations build continuous testing capabilities in-house.

By Organization Size

• SMEs: Typically adopt SaaS-based tools with lower upfront investment and faster time-to-value.
• Large Enterprises: Often require deeply integrated solutions across complex tool chains and may pursue custom implementations.

By End-User Industry

• BFSI (Banking, Financial Services & Insurance): High regulatory and reliability requirements make continuous testing a priority.
• Retail: E-commerce platforms demand high availability and rapid iteration during peak trading periods.
• Manufacturing: Growing software content in industrial equipment and supply chain systems drives testing needs.
• Other sectors: Healthcare, telecom, and government are also significant adopters.

Regional Overview

The continuous testing market has global reach, with regional dynamics varying considerably:

• North America: The most mature market, driven by early DevOps adoption, a large SaaS vendor ecosystem, and strong enterprise IT spending.
• Europe: Solid growth underpinned by digital transformation programs and GDPR-driven quality and audit requirements.
• Asia-Pacific (APAC): Fast-growing region as enterprises in India, China, and Southeast Asia scale their software engineering capabilities.
• Middle East & Africa (MEA): Emerging adoption tied to government-led digital economy initiatives.
• South & Central America: Growing but nascent market, with Brazil and Mexico leading regional uptake.

Why Continuous Testing Matters for DevOps Teams

From a practitioner standpoint, the market growth reflects a genuine shift in how engineering organizations think about quality. Continuous testing is not simply automation for its own sake — it is the mechanism that makes CI/CD pipelines trustworthy. Without it, fast pipelines just deliver broken software faster.

Teams implementing continuous testing typically see improvements in:

• Mean time to detect (MTTD) defects — catching issues in minutes rather than days
• Release confidence — quantified risk scores replace subjective go/no-go decisions
• Developer feedback loops — shift-left testing reduces the cost of fixing defects
• Compliance auditability — automated test evidence replaces manual sign-off documentation

As the market matures, expect continued consolidation among vendors, deeper AI-assisted test generation capabilities, and tighter integration between testing platforms and observability tooling.

The Core Problem: Fixed-Size Boxes (Virtual Machines)

Imagine you are packing a suitcase after a shopping trip. Every item you bought is still inside its original store box — rigid, fixed in size, with plenty of empty space inside each one. You cannot rearrange the contents because everything is sealed in its own box. As a result, you need two suitcases to carry everything home, at a total cost of $200, when all the actual clothing would have fitted in one suitcase ($100) if you had unpacked the boxes and filled every nook and cranny.

Those fixed-size boxes are virtual machines. Every VM carries significant overhead in the form of a full guest operating system — kernel, system libraries, daemons — that consumes CPU and memory before your application even starts. Because VMs are sized to handle peak load (spikes), their average utilisation is often very low. You are paying for space inside boxes that is mostly empty.

Containers: Unpacking the Boxes

A container is the equivalent of removing the clothes from the store boxes and packing them directly into the suitcase. Containers share the host operating system kernel; they do not each carry their own OS. This removes most of the overhead and allows you to pack far more workloads onto the same underlying server or VM.

The key insight is that an application built and run as containers should cost less than the equivalent VM-based deployment — provided those containers are scheduled efficiently onto the available infrastructure. That scheduling problem is exactly what Kubernetes solves.

How Kubernetes Bin-Packing Works

Kubernetes treats each containerised application as a Pod — the smallest deployable unit. When you submit a Pod to the cluster, the Kubernetes scheduler inspects each worker node’s available CPU and memory and places the Pod on the node that best fits its resource requests. This process is called bin packing: filling available capacity as tightly as possible, just like squeezing clothes into every corner of a suitcase.

Because containers have far less overhead than VMs, and because Kubernetes actively optimises placement, you can run many more workloads on the same physical or virtual hardware. Idle capacity that would have been stranded inside individual VMs is now shared across all Pods on the node.

A Rough AWS Cost Comparison

The following numbers are illustrative and simplified. Actual costs depend on instance types, reserved pricing, data transfer, storage, and many other factors. Do not treat these figures as a precise benchmark — use them to understand the order-of-magnitude difference in approach.

Typical VM-Based Setup

Consider a team running 20 application EC2 instances and 4 RDS database instances, all sized for peak load with consistently low average utilisation:

• 20 EC2 instances × ~$100/month = $2,000/month
• 4 RDS instances × ~$250/month = $1,000/month
• Total: ~$3,000/month

Kubernetes on AWS EKS

Migrating those 20 applications to containers and running them as Pods on a single EKS worker node (for example, an m5.xlarge, which AWS allows to run up to 58 Pods based on its network interface IP limits) could look like this:

• 1 × m5.xlarge EC2 worker node × ~$300/month = $300/month
• EKS control plane fee = ~$73/month (check current AWS pricing)
• RDS instances (unchanged) = $1,000/month
• Total: ~$1,373/month

In this illustrative scenario, the EC2 compute cost alone drops from $2,000 to roughly $300 — a reduction of approximately 85% on that line item, and around 54% of the overall bill. In practice, across multiple clients, savings in the range of 60–70% of existing EC2 cloud costs have been reported, particularly where EC2 instances are consistently underutilised. Your mileage will vary based on actual workload characteristics and the instance types you choose.

Note: The RDS line item is excluded from the EC2 saving because managed database services are not typically containerised in the same way. Database migration decisions deserve their own analysis.

Prerequisites: Containerising Your Workloads

Realising these savings is not automatic. Your team must be able to:

1. Containerise each application — write a Dockerfile, build an image, and push it to a container registry (Docker Hub, Amazon ECR, Google Artifact Registry, etc.).
2. Write Kubernetes manifests — define Deployments, Services, ConfigMaps, and resource requests/limits for each workload.
3. Integrate with CI/CD pipelines — automate image builds and cluster deployments so that every commit produces a tagged, versioned image that can be promoted through environments.

This investment in containerisation pays dividends beyond cost reduction. A tagged container image is a portable, reproducible artifact that runs identically on a developer laptop, an on-premises cluster, or any cloud provider’s managed Kubernetes service. It also enables advanced deployment strategies out of the box:

• Blue/Green deployments — run two identical environments, switch traffic instantly, roll back immediately if something goes wrong.
• Canary releases — route a small percentage of traffic to a new version before rolling it out fully.
• Rolling updates — Kubernetes replaces Pods incrementally with zero downtime by default.

Beyond Cost: Other Reasons to Adopt Kubernetes

Cost optimisation is a compelling starting point, but Kubernetes offers a broader set of operational benefits:

• Portability — workloads are no longer coupled to a specific cloud provider or hypervisor.
• Self-healing — Kubernetes automatically restarts failed containers and reschedules Pods away from unhealthy nodes.
• Horizontal scaling — the Horizontal Pod Autoscaler can scale your application Pods based on CPU, memory, or custom metrics, and the Cluster Autoscaler can add or remove worker nodes to match demand.
• Declarative configuration — your entire infrastructure state is described in version-controlled YAML files, making it auditable and reproducible.
• Ecosystem — a large ecosystem of Helm charts, operators, and CNCF projects covers observability, networking, security policy, and more.

Is Kubernetes Right for Every Workload?

Kubernetes introduces operational complexity. Small teams running a handful of simple services may find that the learning curve and cluster management overhead outweigh the savings. Managed services like AWS EKS, Google GKE, and Azure AKS reduce — but do not eliminate — that burden. Before committing, assess:

• Whether your applications can be containerised without significant refactoring.
• Your team’s current Kubernetes expertise or appetite for training.
• Whether your workloads are genuinely underutilising their current VMs (check actual CPU and memory metrics, not just assumed peak sizing).
• Stateful workloads — databases and other stateful services require additional planning (persistent volumes, operators) and may be better left on managed cloud database services.

Summary

The suitcase analogy makes the economic argument for Kubernetes intuitive: VMs are like fixed-size boxes that waste space; containers are like loose items you can pack tightly. Kubernetes is the expert packer that fills every available gap. For organisations running underutilised VM fleets — especially on public cloud infrastructure where every idle CPU cycle costs money — migrating workloads to a Kubernetes cluster can yield substantial and measurable cost reductions, while simultaneously improving deployment velocity and operational resilience.

Original concept and suitcase analogy attributed to Owen Rogers (451 Research), as discussed on the Google Kubernetes Podcast.

Overview: Google Cloud Next ’20 Goes Fully Digital

Google Cloud announced the transformation of its flagship annual conference into Google Cloud Next ’20: OnAir — a free, nine-week, fully digital event series. The event runs from July 14 through September 8, 2020, delivering new content each week rather than compressing everything into a single multi-day conference.

The shift to an on-demand, weekly format is a direct response to the constraints of 2020, but it also presents a practical advantage for DevOps and platform engineering teams: sessions can be consumed at your own pace, on your own schedule, without the need to block out an entire week or budget for travel.

What the Event Includes

Google Cloud Next ’20: OnAir is organized around a nine-week editorial calendar, with each week focusing on a distinct topic area relevant to cloud and DevOps practitioners. Across the full series, attendees have access to:

• 200+ sessions spanning keynotes, breakout sessions, and expert deep-dives
• Weekly keynotes from Google Cloud leadership and industry figures
• Advanced technical sessions covering infrastructure, security, data analytics, application modernization, AI/ML, collaboration, and productivity
• Live Q&A sessions and the ability to book one-on-one time with Google engineers and developer advocates
• A Digital Showcase featuring live product demos and interactive technology experiences

Hands-On Learning and Certification Preparation

For DevOps engineers looking to level up their Google Cloud skills or pursue certification, the event includes several structured learning programs:

• Study Jams — guided group learning sessions to help you prepare for Google Cloud certifications
• Cloud Hero — a gamified, competitive hands-on labs experience
• Free one-month access to Qwiklabs and Pluralsight — curated learning paths aligned to certification tracks, available to all registered attendees
• Live technical sessions with the Google Cloud Developer Relations team

The combination of self-paced labs, live expert access, and free platform access makes this a practical opportunity to build verifiable skills alongside the conference content.

Registration and Access

The event is free to attend. Engineers who were already registered for the in-person Next ’20 were automatically enrolled in OnAir and did not need to re-register. New attendees could register directly on the Next OnAir website.

Registering provides access to:

• A customizable personal schedule builder to plan your nine-week viewing agenda
• Networking and engagement features to connect with Googlers, Google Cloud partners, and peers in the broader cloud community
• Session recommendations based on your role and interests

Weekly Topic Format

Rather than a single broad agenda, OnAir structures content into weekly themes. According to the original announcement, the nine-week schedule covers distinct topic areas relevant to cloud adoption and engineering — including infrastructure, security, data, and application modernization — though the full published schedule was provided via a separate linked asset not reproduced here.

This format allows teams to coordinate viewing around the weeks most relevant to their current projects, making it easier to extract actionable takeaways without consuming the entire catalog.

Why This Matters for DevOps Teams

The OnAir format reflects a broader industry trend toward asynchronous, modular technical education. For DevOps engineers specifically, the relevant sessions on infrastructure modernization, CI/CD, security posture, and platform engineering are typically buried inside large conference schedules. The weekly topic structure makes it easier to identify and prioritize the content that maps to active engineering work.

The free access to Qwiklabs and Pluralsight is a concrete, time-limited benefit worth taking seriously if your team has certification goals for Google Cloud Associate, Professional, or specialty tracks in 2020.

Note: This article is based on the original Google Cloud announcement from May 2020. Event dates, registration links, and session availability were specific to that event cycle and are no longer active.

Why IT Infrastructure Monitoring Matters

Infrastructure monitoring tools give operations teams continuous visibility into the health and performance of servers, networks, storage, applications, and cloud services. Without them, outages are detected by end users rather than by the team responsible for uptime. Key business drivers pushing adoption include:

• Shift to hybrid and multi-cloud architectures — Teams need a single pane of glass that spans on-premises hardware and cloud providers simultaneously.
• Rising complexity of microservices and containers — Ephemeral workloads require dynamic discovery and auto-instrumentation, capabilities older tools struggle to provide.
• Stricter compliance and SLA requirements — Financial services, healthcare, and government sectors demand audit trails and availability reporting.
• Cost of downtime — Unplanned outages carry significant financial and reputational risk, making proactive monitoring a clear ROI story.

Major Vendors in the Space

The following platforms appear consistently in enterprise evaluations and open-source shortlists. Each has a distinct positioning:

SolarWinds

SolarWinds offers a broad portfolio — Network Performance Monitor (NPM), Server & Application Monitor (SAM), and the unified Orion platform — that suits mid-market and enterprise environments with mixed on-premises infrastructure. Its agent-based and agentless collection options make it flexible for heterogeneous environments.

ManageEngine (OpManager)

ManageEngine’s OpManager provides network, server, and application monitoring with deep integration into the broader Zoho/ManageEngine ITSM ecosystem. It is a popular choice for organizations that want to correlate monitoring alerts with service desk tickets in a single vendor relationship.

Zabbix

Zabbix is a mature open-source platform with enterprise-grade features including distributed monitoring, high-availability configurations, and flexible alerting. Because it carries no licensing cost, it sees strong adoption in cost-conscious environments and large-scale deployments where teams have the staff to manage it.

Paessler PRTG

PRTG’s sensor-based licensing model and polished dashboards make it especially accessible for smaller IT teams. It monitors networks, servers, applications, and IoT devices through a large library of pre-built sensors, and is commonly deployed in manufacturing and industrial environments.

Datadog

Datadog is a cloud-native, SaaS-delivered monitoring and observability platform. Its agent integrates with over 700 technologies out of the box, and it unifies infrastructure metrics, APM traces, log management, and synthetic monitoring in a single interface. It is commonly adopted by organizations with significant cloud or container footprints.

Nagios

Nagios Core is one of the oldest and most widely deployed open-source monitoring tools. Nagios XI, its commercial edition, adds a web UI, reporting, and configuration wizards. While its plugin ecosystem is enormous, teams often need to invest time in customization compared with newer all-in-one platforms.

Other Notable Vendors

• VMware Aria Operations (formerly vRealize Operations) — Deep integration with vSphere and VMware Cloud environments.
• PagerDuty — Primarily an incident management and on-call scheduling platform that aggregates alerts from monitoring tools rather than collecting metrics itself.
• Catchpoint — Focuses on digital experience monitoring (DEM), synthetic testing, and real-user monitoring from a global vantage-point network.
• LogicMonitor — SaaS-based infrastructure monitoring targeting managed service providers (MSPs) and enterprise IT teams with agentless, auto-discovered monitoring.
• ScienceLogic SL1 — Designed for large, complex environments with strong emphasis on AIOps-driven root-cause analysis and multi-tenant architectures.
• Kaseya VSA — An RMM (Remote Monitoring and Management) platform oriented toward MSPs managing customer endpoints and infrastructure.
• Micro Focus (now OpenText) Operations Bridge — Enterprise-grade AIOps and event correlation for large data centers.
• Ipswitch WhatsUp Gold — Network-focused monitoring with an accessible interface suited to SMB and mid-market teams.
• NetApp Cloud Insights — Infrastructure and storage performance analytics, especially strong in NetApp-heavy environments.
• TeamViewer / Xmatters — Typically used for remote access/support and alert notification workflows respectively, rather than as primary monitoring collectors.
• Virtual Instruments (now Virtana) — Specializes in hybrid infrastructure performance management, particularly storage and workload analytics.

Deployment Models

Cloud-Based (SaaS)

SaaS-delivered tools such as Datadog, LogicMonitor, and Catchpoint offload infrastructure management to the vendor. Benefits include rapid onboarding, automatic feature updates, elastic scaling, and no on-premises server footprint. The trade-off is that all telemetry travels to a third-party cloud, which can raise data residency or compliance concerns for regulated industries.

On-Premises

Self-hosted platforms like Zabbix, Nagios, and PRTG keep all data within the organization’s network. This is preferred when strict data sovereignty requirements apply, when airgapped environments must be monitored, or when a large existing data center makes SaaS egress costs prohibitive. The cost model shifts from subscription to staff time and infrastructure.

Industry Verticals and Use Cases

How to Evaluate an IT Infrastructure Monitoring Tool

Use the following criteria when shortlisting platforms for a proof of concept:

1. Coverage breadth — Does it natively monitor your OS types, hypervisors, network vendors, cloud providers, and SaaS applications without requiring custom scripting for every integration?
2. Scalability — Can the platform handle your current host count and project forward two to three years without a re-architecture?
3. Alerting and noise reduction — Does it support alert correlation, dependency-aware suppression, and anomaly detection to reduce alert fatigue?
4. Visualization and dashboards — Are out-of-the-box dashboards useful, and how easy is it to build custom views for different stakeholder audiences?
5. ITSM and ChatOps integrations — Does it route alerts to your ticketing system (Jira, ServiceNow) and messaging tools (Slack, Teams) natively?
6. Total cost of ownership — For open-source tools, factor in staffing and customization time. For SaaS, model cost against host count and data retention requirements.
7. Security and compliance posture — Review SOC 2, ISO 27001, FedRAMP, or GDPR compliance documentation depending on your regulatory context.
8. Vendor support and community — A large community (Zabbix, Nagios) can offset the cost of formal support contracts; for critical SLAs, evaluate vendor response time commitments.

SWOT and PESTEL Considerations for Buyers

Market analysts apply SWOT (Strengths, Weaknesses, Opportunities, Threats) and PESTEL (Political, Economic, Social, Technological, Environmental, Legal) frameworks to assess market dynamics. From a practitioner’s perspective, these translate into practical buying considerations:

• Strengths to look for: Deep integration ecosystem, proven scalability, strong documentation.
• Weaknesses to watch: Vendor lock-in, opaque pricing, poor support for modern container environments in older platforms.
• Opportunities: AIOps capabilities that reduce mean time to detect (MTTD) and mean time to resolve (MTTR).
• Threats: Consolidation in the vendor landscape (acquisitions can shift roadmap priorities); open-source alternatives can erode the case for expensive licenses.
• Legal/compliance factors: Data residency laws (GDPR, CCPA) may mandate on-premises or region-specific SaaS deployments.
• Economic factors: Budget pressure drives interest in open-source and freemium tiers; cloud-native growth drives SaaS adoption.

Summary

The IT infrastructure monitoring tool market is large, mature, and still evolving rapidly — particularly as observability (metrics, logs, and traces in a unified platform) displaces traditional siloed monitoring approaches. Whether you are a small team evaluating Zabbix or PRTG for an on-premises data center, or an enterprise weighing Datadog against ScienceLogic for a multi-cloud deployment, the evaluation criteria above provide a practical starting framework. Pilot any shortlisted tool against your actual environment before committing to a licensing agreement.

What Is the Hybrid Cloud?

At its simplest, a hybrid cloud is a combination of on-premises and off-premises IT resources. More precisely, it refers to a computing environment made up of on-premises private cloud infrastructure combined with third-party public cloud services, integrated through some form of orchestration layer that allows workloads and data to move between the two.

The defining characteristic of a hybrid cloud is that workloads and data can shift between private and public environments in a flexible, policy-driven way. This allows organisations to respond to changes in demand, cost, and compliance requirements without being locked into a single infrastructure model.

Key Benefits of the Hybrid Cloud Model

Flexibility and Scalability

The primary advantage of the hybrid cloud is flexibility. On-premises infrastructure requires advance planning to expand capacity and carries significant capital cost. The public cloud, by contrast, can provision resources on demand with minimal lead time.

A pattern commonly associated with hybrid architectures is cloud bursting — the practice of temporarily offloading workloads to a public cloud when demand exceeds the capacity available in the private cloud. Typical use cases include:

• Seasonal traffic spikes — e-commerce platforms handling peak holiday demand, for example, can absorb excess load in a public cloud without over-provisioning private infrastructure for the rest of the year.
• Geographic or event-driven demand variations — sudden surges driven by location, marketing campaigns, or external events.
• Batch and analytics workloads — computationally intensive jobs that run periodically and do not justify permanent on-premises capacity.

For data storage specifically, on-premises private cloud storage typically offers the lowest latency and highest throughput for frequently accessed, performance-sensitive data. Data that is accessed infrequently, or that does not require the lowest possible latency, can be tiered to public cloud storage where it remains accessible but at a lower cost.

Cost Optimisation

The hybrid model allows IT teams to match workloads to the most cost-effective resource available. Public cloud removes the need for capital expenditure on hardware and reduces ongoing operational overhead. At the same time, retaining a private cloud component for predictable, steady-state workloads can be more economical than running those workloads in the public cloud long-term.

Services can be scaled up, redeployed, or scaled down as business needs change, avoiding the expense of idle on-premises capacity and reducing the risk of over-provisioning.

Data Control and Compliance

Regulated industries — financial services, healthcare, government — often face requirements that make it impractical to move all workloads to the public cloud. A hybrid model allows sensitive data and regulated workloads to remain on-premises or in a private environment that meets compliance requirements, while less sensitive workloads take advantage of public cloud economics and agility.

Limitations: When Hybrid Cloud Is Not the Right Fit

Hybrid cloud is not the right solution for every organisation or workload. Key limitations to consider include:

• Cost of private infrastructure — Setting up and maintaining on-premises private cloud infrastructure involves significant capital and operational expense. Smaller organisations operating on constrained IT budgets may find that an entirely public cloud solution is more cost-effective.
• Latency-sensitive applications — Applications that require the absolute lowest latency may not perform adequately when workloads or data must traverse the network boundary between private and public environments. The impact depends heavily on the specific workload type: latency tends to matter more for real-time compute workloads than for bulk data transfers.
• Complexity of management — Operating across two or more environments introduces integration, security, and governance complexity. Without the right tooling and skills, this overhead can outweigh the flexibility benefits.
• Workload analysis is essential — Hybrid cloud delivers the best results when each application and workload has been examined to determine the optimal placement — private cloud, public cloud, or traditional on-premises IT. Without that analysis, organisations risk placing workloads in sub-optimal environments, negating the cost and performance advantages.

Deciding Whether to Go Hybrid

The decision to adopt a hybrid cloud model should be driven by a structured assessment of your organisation’s workloads, compliance requirements, budget, and operational capabilities — not by industry trends alone.

A hybrid approach is likely worth considering if:

• You have workloads with highly variable demand that do not justify permanent on-premises capacity.
• You have regulatory or data-sovereignty requirements that constrain certain workloads to controlled environments.
• You maintain legacy systems that cannot be easily migrated to the public cloud but need to interoperate with cloud-native services.
• You want to modernise gradually rather than through a single, high-risk migration.

A purely public cloud model may be preferable if:

• Your organisation is small or has no existing on-premises infrastructure worth preserving.
• You have no compliance requirements that mandate private infrastructure.
• The cost and complexity of managing private cloud would outweigh the flexibility it provides.

Planning a Successful Hybrid Cloud Implementation

For organisations that do move forward with a hybrid cloud architecture, thorough planning is critical. Specific areas to address include:

• Workload classification — Categorise workloads by performance requirements, data sensitivity, compliance constraints, and cost profile before assigning them to an environment.
• Networking and connectivity — The performance characteristics of the link between your private and public environments will directly affect latency-sensitive workloads. Direct connectivity options (such as dedicated private links to major cloud providers) can mitigate this.
• Security and identity — A unified identity and access management approach across both environments reduces the risk of security gaps at the boundary.
• Orchestration and automation — Tools that provide a consistent operational model across private and public environments — for deployment, monitoring, scaling, and cost management — are essential for managing hybrid complexity efficiently.
• Governance and cost visibility — Without clear visibility into spending across both environments, cost optimisation is difficult. Implement tagging strategies and centralised cost management from the outset.

Summary

The hybrid cloud model offers genuine advantages — flexibility, cost optimisation, and the ability to bridge on-premises constraints with public cloud capabilities — but it is not universally the right answer. Its value depends on workload characteristics, organisational scale, compliance requirements, and the operational maturity needed to manage a more complex environment.

Organisations that approach hybrid cloud with a structured workload assessment and a clear integration strategy are well positioned to build an infrastructure that adapts to changing business demands while controlling costs and maintaining the control they need over critical data and systems.

What Are DevOps Tools?

DevOps tools are software platforms and utilities designed to break down silos between development, operations, and quality-assurance teams. By standardising and automating the movement of code through build, test, and deployment pipelines, they accelerate the software delivery lifecycle while reducing human error. Common functional categories include:

• Source control and collaboration – version control systems and code-review platforms
• Continuous integration / continuous delivery (CI/CD) – automated build and release pipelines
• Configuration management and infrastructure-as-code – tools that provision and maintain environments declaratively
• Monitoring and observability – platforms that provide real-time insight into application and infrastructure health
• Container orchestration – systems that manage containerised workloads at scale

Market Overview

The global DevOps tool market experienced sustained expansion heading into 2020, driven by enterprises accelerating their digital transformation programmes. The market spans tooling sold as on-premises software, cloud-based SaaS, and hybrid deployments, and is consumed across virtually every industry vertical — from financial services to healthcare to retail.

COVID-19 introduced both disruption and acceleration. Organisations that had invested in DevOps practices and cloud-native tooling were better positioned to shift their workforces to remote work quickly, maintain deployment velocity, and adapt products to rapidly changing customer needs. This dynamic is widely credited with accelerating cloud and DevOps adoption among organisations that had been slower to modernise.

Key Market Drivers

Several forces are pushing enterprise investment in DevOps tooling:

• Enterprise digitalisation – Organisations across manufacturing, retail, and financial services are automating business processes, increasing demand for reliable software delivery pipelines.
• Agile and cloud adoption – The widespread shift to agile methodologies and cloud-native architectures requires tooling that supports iterative, frequent releases.
• Collaboration between IT disciplines – Growing pressure to reduce mean time to recovery (MTTR) and accelerate feature delivery is pushing teams toward shared toolchains and shared responsibility models.
• IoT, mobility, and BYOD initiatives – Expanding device ecosystems increase the complexity of software environments, making automated testing and deployment more valuable.
• Cost reduction through automation – Automating repetitive tasks in testing, provisioning, and deployment delivers measurable reductions in engineering overhead.
• R&D investment by major vendors – Leading platform vendors continue to invest in new capabilities — particularly around AI-assisted operations (AIOps), security integration (DevSecOps), and low-code pipeline configuration.

Market Restraints

Despite strong tailwinds, the market faces meaningful challenges:

• Skills shortage – Demand for engineers with deep DevOps expertise consistently outpaces supply, slowing adoption particularly in mid-market organisations.
• Lack of standardisation – The DevOps toolchain ecosystem remains fragmented. Organisations frequently assemble bespoke stacks from dozens of point solutions, increasing integration complexity and total cost of ownership.
• Cultural resistance – Tooling adoption alone does not produce DevOps outcomes; organisational change management is a persistent barrier that technology vendors cannot solve on their own.

Market Segmentation

By Deployment Mode

Cloud-based (SaaS) DevOps tools are projected to record the strongest growth across the forecast period. The primary reasons are lower upfront capital expenditure, elastic scalability, built-in high availability, and reduced operational burden on internal IT teams. Cloud deployment also aligns naturally with modern containerised and microservices architectures, making it the default choice for greenfield projects and cloud-migration programmes.

By Enterprise Size

Large enterprises account for a significant share of market revenue. Their complex, multi-team engineering organisations and high deployment frequency create both the need and the budget for comprehensive, integrated DevOps platforms. However, mid-market and SMB adoption is growing as SaaS delivery models reduce the barrier to entry.

By Industry Vertical

The IT sector is the largest consumer of DevOps tooling — unsurprisingly, given that software delivery is the core business. Banking, Financial Services, and Insurance (BFSI) is the second-largest vertical, driven by the rapid growth of online and mobile banking, rising consumer expectations for digital services, and the competitive pressure to release features faster than fintech challengers.

Regional Analysis

North America and Europe

North America holds the largest share of global DevOps tool market revenue, reflecting the density of large technology companies, mature cloud infrastructure, and early enterprise adoption of agile and DevOps practices. Europe follows, supported by significant digital investment across financial services, manufacturing, and the public sector, as well as active regulatory pressure to improve software quality and auditability.

Asia Pacific

Asia Pacific represents the fastest-growing regional market. Rapid smartphone penetration, expanding digital infrastructure, and technology investment by governments and large enterprises in China, India, Japan, and South-East Asia are all contributing to rising demand for DevOps tooling. The region’s large base of software development talent is also a structural advantage for DevOps adoption.

Competitive Landscape

Key vendors operating in the global DevOps tool market are pursuing several strategic approaches to maintain and grow market position:

• Mergers and acquisitions – Platform vendors are acquiring specialist point-solution providers to broaden their toolchain coverage and reduce the integration burden on customers.
• Partnership ecosystems – Collaboration with cloud hyperscalers, system integrators, and consultancies extends market reach and embeds products into large enterprise programmes.
• Innovation labs and centres of excellence – Investment in internal research and partnerships with academic institutions is producing advances in AI-driven testing, predictive deployment risk assessment, and automated remediation.

COVID-19 Impact on the DevOps Tool Market

The pandemic accelerated several trends already present in the market:

• Remote-first engineering – Distributed teams increased reliance on cloud-hosted CI/CD pipelines, collaborative code-review platforms, and chat-integrated deployment workflows.
• Faster digital product iteration – Businesses pivoting to digital channels needed to ship new features and integrations at unprecedented speed, raising the value of automated delivery pipelines.
• Budget reallocation – Some organisations deferred on-premises hardware refreshes in favour of cloud-based tooling, accelerating the SaaS deployment trend.
• Security scrutiny – The sudden expansion of remote access surfaces increased focus on DevSecOps — integrating security scanning and policy enforcement directly into the delivery pipeline.

Key Takeaways

The global DevOps tool market in 2020 is characterised by strong structural growth, a fragmented but consolidating vendor landscape, and an accelerating shift to cloud-based delivery. The COVID-19 pandemic acted as a forcing function for digital transformation, benefiting organisations with mature DevOps practices while exposing gaps in those still reliant on manual, on-premises processes. For engineering leaders, the near-term priorities are toolchain consolidation, investment in developer skills, and integrating security earlier in the software delivery lifecycle.

Source: Originally reported by 3wnews.org, citing research by Trusted Business Insights.

How the Pandemic Reshaped Cloud Adoption

When COVID-19 forced hundreds of millions of office workers indoors in early 2020, enterprises that had been slowly migrating to cloud infrastructure suddenly had no choice but to accelerate. Overnight, cloud computing stopped being a long-term strategy discussion and became an operational necessity. Video conferencing, remote project collaboration, online education, e-commerce, and streaming services all depend on elastic cloud infrastructure — and all of them experienced sudden, simultaneous demand spikes.

Major technology employers reinforced the shift. Twitter announced in May 2020 that employees could work from home permanently if they chose, with offices remaining closed to non-essential staff until at least September of that year. Facebook and Alphabet (Google’s parent company) extended their own remote-work policies through the end of 2020. These decisions signalled that the transition was not temporary, and that the infrastructure supporting remote work — cloud compute, storage, collaboration platforms — would need to scale accordingly.

Hyperscaler Infrastructure Expansion in 2020

Before the pandemic, enterprises were already consolidating away from on-premises data centers toward hyperscaler platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. The shift accelerated sharply once remote work became mandatory at scale.

Microsoft Azure

Microsoft announced plans in early 2020 to invest under $1.5 billion in its first Italian data center region and approximately $1 billion in a Polish data center. An additional region in Spain had been announced in February 2020. At the time, Microsoft operated 60 cloud regions globally. Azure reported 59% revenue growth in its fiscal Q3 2020, while Office 365 Commercial and Dynamics 365 grew 25% and 47% respectively — figures that reflected surging enterprise demand for cloud-delivered productivity and business applications.

Amazon Web Services

Amazon opened a new data center region in Italy around the same period. AWS also announced significant improvements to Amazon Macie, its managed data security and privacy service, including expanded global availability and reduced pricing. These moves reflected AWS’s strategy of growing regional coverage to address data residency requirements while making security tooling more accessible at scale.

Oracle Cloud

Oracle was preparing to launch its second cloud region in South Korea (Chuncheon) before the end of May 2020, expanding its footprint in the Asia-Pacific market. In April 2020, Zoom entered a partnership with Oracle to support its rapidly expanding cloud infrastructure needs — a notable win for Oracle Cloud Infrastructure (OCI) and a visible signal of the demand pressures cloud providers were managing at the time.

Cloud Computing ETFs: A Financial Lens on the Sector

For engineers and technical leaders who also follow investment markets, cloud computing exchange-traded funds (ETFs) offer a way to track the financial health and relative growth of the sector as a whole. These funds aggregate exposure across multiple hyperscalers, SaaS companies, and cloud infrastructure vendors, which makes their performance a useful proxy for sector-wide sentiment.

Important disclaimer: Nothing in this article constitutes investment advice. The fund details below are presented as context for understanding market structure, not as recommendations.

First Trust Cloud Computing ETF (SKYY)

SKYY seeks to track the ISE Cloud Computing Index, which covers companies actively involved in the cloud computing industry. As of mid-2020, the fund held approximately 65 securities, with Amazon, Microsoft, Oracle, and Alphabet among the top ten holdings. Its reported assets under management were approximately $3.56 billion, with an expense ratio of 0.60%. The fund had returned roughly 5.5% year-to-date as of the original article’s publication date in May 2020. These figures are historical and will have changed substantially since then.

The composition of funds like SKYY is relevant to DevOps practitioners because it reflects which companies analysts and index providers consider to be the primary engines of cloud infrastructure — the same platforms on which most production workloads are deployed.

Why Cloud Infrastructure Growth Matters for DevOps Teams

Financial performance of cloud providers is not just a stock market story — it has direct implications for engineering teams:

• Regional expansion means lower latency options and better data residency compliance for globally distributed teams and their customers.
• Competitive pricing pressure between AWS, Azure, and GCP has historically driven down per-unit compute and storage costs, benefiting engineering budgets.
• Investment in managed services (such as the Amazon Macie improvements noted above) reduces the operational overhead DevOps and security teams carry.
• Partnership announcements (such as Zoom + Oracle) often signal new integrations, certified architectures, or migration tooling that practitioners can take advantage of.

The Long-Term Signal

The COVID-19 pandemic acted as a forcing function for cloud adoption that had been underway for years. Enterprises that had planned multi-year migration roadmaps compressed those timelines significantly. The hyperscalers responded with accelerated infrastructure investment, new regional deployments, and aggressive managed-service development. That investment cycle — reflected in the financial performance tracked by cloud ETFs — shaped the tooling and platform capabilities that DevOps engineers work with today.

For practitioners, the practical takeaway is that cloud providers with strong revenue growth reinvest heavily in platform capabilities, reliability, and tooling. Understanding the financial dynamics of the sector helps engineers anticipate where platform investment is concentrated — and where the next generation of managed services, developer tooling, and automation capabilities is likely to emerge.

Original source referenced: finance.yahoo.com (May 2020). Financial data, growth figures, and company announcements reflect conditions at that date.

Linode Kubernetes Engine Goes Generally Available

Linode announced the general availability of the Linode Kubernetes Engine (LKE), a fully managed Kubernetes service that provides teams with access to a Kubernetes control plane at no additional cost. The move positioned Linode as a cost-competitive alternative to larger cloud providers that charge separately for control plane management.

Howie Ross, Director of Engineering at Linode, stated that the company’s goal was to offer enterprise-grade Kubernetes cluster management at a price point not matched by the major cloud hyperscalers, where control plane access is frequently billed as a distinct, premium service.

Supported Kubernetes Versions and Regions at Launch

At general availability, LKE supported the following Kubernetes versions:

• Kubernetes 1.17
• Kubernetes 1.16
• Kubernetes 1.15

Clusters were available in four data center regions at launch:

• Dallas, TX
• Fremont, CA
• Newark, NJ
• Sydney, Australia

Linode indicated plans to expand LKE availability to additional data centers based on customer demand.

What the LKE Control Plane Includes

The managed control plane exposed through LKE comes with a meaningful set of built-in capabilities, reducing the operational overhead typically associated with self-managed Kubernetes deployments:

• Linode Cloud Controller Manager – Automates Kubernetes version upgrades and provides backup and recovery tooling.
• Kubernetes API server – Standard API access for cluster management and automation.
• Scheduler – Manages workload placement across nodes.
• etcd – The open-source distributed key/value store used for cluster state persistence.
• Resource controllers – Standard Kubernetes controllers for managing deployments, replica sets, and other resources.

Storage and Networking Integration

LKE ships with production-ready integrations for both storage and networking:

• Linode Container Storage Interface (CSI) driver – Enables dynamic provisioning and management of Linode block storage volumes directly from within Kubernetes workloads.
• NodeBalancer integration – Allows teams to provision and configure Linode’s load balancers automatically through Kubernetes Service objects of type LoadBalancer.

Third-Party Tooling Support

LKE launched with integrations for widely adopted DevOps and infrastructure tools:

• Helm – For Kubernetes package management and application templating.
• Terraform – For infrastructure-as-code provisioning of LKE clusters and associated resources.
• Rancher Labs – For teams that prefer Rancher’s multi-cluster management interface on top of LKE.

Why Managed Kubernetes Matters for DevOps Teams

Running Kubernetes in production involves considerably more than standing up a cluster. Teams must manage the control plane, handle upgrades, configure networking and storage integrations, and maintain observability and backup pipelines. For many organizations, this operational burden is a barrier to adoption.

Managed services like LKE offload control plane responsibilities to the provider, allowing engineering teams to focus on application delivery rather than cluster operations. Ross highlighted that LKE was designed to allow teams to deploy applications on Kubernetes with minimal setup friction.

The cost angle is also significant: providers like AWS, Google Cloud, and Azure have historically charged for managed control plane access (for example, Amazon EKS charges a per-cluster fee). Linode’s decision to include the control plane at no extra cost directly targets cost-sensitive teams and smaller organizations running Kubernetes workloads.

The Broader Kubernetes Adoption Context

The LKE launch came during a period of growing interest in container orchestration, with organizations increasingly recognizing Kubernetes as the de facto standard for running scalable, resilient applications. At the same time, self-managed Kubernetes deployments remain complex and resource-intensive, creating demand for managed offerings that reduce operational overhead without sacrificing flexibility.

Kubernetes’ ability to scale infrastructure up and down on demand also made it particularly relevant for organizations reassessing their application architecture for resilience and agility.

Summary

Note: LKE features, supported Kubernetes versions, pricing, and regional availability have likely changed since this original announcement in May 2020. Refer to Linode’s official LKE documentation for current details.

The Problem Kasten Was Built to Solve

Like many successful startups, Kasten emerged from direct, personal experience with a hard problem. Co-founders Niraj Tolia and Vaibhav Kamra joined EMC through the 2014 acquisition of Maginatics, a company that provided a secure global namespace across storage platforms. When Tolia’s team began migrating their product’s control plane to Google Cloud and Kubernetes in 2015, the experience was painful enough to spark an idea.

“We were discovering integration issues much earlier in the lifecycle of product development than what we’ve traditionally seen,” Tolia explained. “This is five years ago, so that’s when the lightbulb went off in my head that this is going to be the future.”

At EMC, the team was working within the data protection group and found that existing tooling simply didn’t scale to Kubernetes workloads. Scripts were hand-crafted, brittle, and impractical at any meaningful scale. Kasten was founded in 2017 — its name taken from the German word for “box” or “container” — to build data management tooling that was truly cloud-native from day one.

Why Traditional Backup Tools Fail in Kubernetes Environments

Traditional data management platforms were designed for virtual machines with relatively static configurations. Kubernetes changes the fundamental assumptions those tools rely on:

• Component explosion: Tolia describes a roughly 100x increase in the number of discrete components that need to be captured compared to VM-based workloads.
• Frequent rescheduling: Pods are ephemeral and can be rescheduled at any time, making point-in-time snapshots taken at the infrastructure layer incomplete or inconsistent.
• No VM-to-application mapping: Legacy backup tools rely on VM identity to understand application boundaries. In Kubernetes, that mapping doesn’t exist.
• Network policy conflicts: Some legacy vendors require firewall exceptions or open network policies to access cluster internals — an approach that directly conflicts with Kubernetes security models.
• Constantly changing applications: Microservice architectures deploy frequently, so backup tooling must continuously rediscover application topology rather than relying on static configuration.

The K10 Platform: Architecture and Key Concepts

Kasten’s K10 platform is itself deployed as a Kubernetes application — what Tolia describes as a “critical cluster” pattern. K10 installs into its own namespace and uses a Kubernetes service account to discover and orchestrate other applications running on the same cluster. It also integrates with the underlying physical storage infrastructure.

Application Auto-Discovery

K10 uses Kubernetes-native APIs to automatically discover containerized applications, their components, startup processes, and associated policies. Rather than requiring manual inventory or agent installation on each workload, discovery is driven by the Kubernetes control plane itself.

Restore Points: More Than a Data Snapshot

A core K10 concept is the restore point — an atomic unit that captures not just volume data but the complete application state within a Kubernetes namespace, including:

• Kubernetes resource definitions (Deployments, Services, StatefulSets, etc.)
• Secrets and ConfigMaps
• Container image references
• Service account bindings
• Networking configuration

This dependency tree means a restore point can be used to fully reconstruct an application on a different cluster, region, or cloud provider without manual reconfiguration.

CSI Integration and Storage Agnosticism

K10 uses the Kubernetes Container Storage Interface (CSI) abstraction layer to interact with block and object storage systems. This means K10 does not need to understand the proprietary APIs of individual storage arrays. It works with a wide variety of storage backends and is designed to be storage-agnostic — it is not a storage system itself but a data management layer on top of existing infrastructure.

Blueprints: Agentless Extension for Complex Workloads

For workloads that require coordination across multiple services before a backup can be taken consistently — such as flushing a database write-ahead log — K10 provides an agentless extension mechanism called blueprints. Blueprints allow operators to define pre-backup preparation steps and post-restore transformation steps without modifying the application itself or its deployment pipeline.

Blueprint transforms are particularly useful during migrations: for example, updating DNS names, regenerating TLS certificates (such as those issued by Let’s Encrypt), or adjusting environment-specific configuration values as an application moves from production to a test environment.

Typical Backup and Migration Workflow

The K10 workflow for backup and cross-cluster migration follows a consistent pattern:

1. Scope a namespace — select the Kubernetes namespace representing the application to protect.
2. Run a backup action — K10 creates a restore point capturing data and all associated Kubernetes resources.
3. Run an export action — the restore point is exported to an object store (for example, Amazon S3 or a compatible endpoint).
4. Apply a backup policy — schedule the above steps on an hourly, daily, or weekly cadence.
5. Create an import policy on the destination cluster — the import action retrieves the exported restore point.
6. Run a restore action — the namespace, data, and configuration are recreated on the destination cluster, optionally with blueprint transforms applied.

This workflow applies equally to disaster recovery, environment promotion (production to staging), and full cluster migration.

Real-World Use Case: Sopra Steria’s OpenShift Migration

Sopra Steria, a UK-based IT consultancy, used K10 when migrating close to 200 applications from OpenShift 3.11 to OpenShift 4.3. Lead DevOps architect Michael Courcy described the process in a webinar, noting that K10’s restore-point model — combining data snapshots with full Kubernetes resource state — made it practical to migrate applications at scale while accommodating infrastructure differences between the source and destination clusters, including DNS name changes handled via blueprint transforms.

Balancing Operator and Developer Needs

K10 is designed to serve two audiences with different priorities:

• Operations teams get policy-driven automation, global visibility across multiple clusters, alerting, and compliance management without manual per-application configuration.
• Development teams are largely unaffected — K10 requires zero changes to application code or deployment pipelines. Developers who want finer control can optionally use blueprints to customise backup and restore behaviour for their specific workloads.

Cloud Native Transformation Framework

Kasten introduced the Cloud Native Transformation Framework as part of its Application Transformation Engine. According to the company, this layer adds capabilities including:

• Capture of underlying data and additional metadata
• Lock-free algorithms to reduce backup contention
• Pluggable encryption and compression
• Advanced deduplication to reduce storage consumption
• Smaller fault domains to improve backup reliability

Deployment and Scalability

K10 runs both on-premises and in public cloud environments. Because it is itself a Kubernetes application, it scales with the workload and does not require dedicated backup infrastructure separate from the cluster it protects. This can reduce overall IT infrastructure footprint compared to traditional backup appliances or agents.

Summary

Kasten K10 addresses a genuine gap: the mismatch between Kubernetes’ dynamic, namespace-scoped application model and the VM-centric assumptions baked into legacy backup tools. By treating a Kubernetes namespace as the unit of protection — capturing data, configuration, and resource state together — K10 makes backup, restore, disaster recovery, and cross-cluster migration operationally tractable for teams running stateful workloads on Kubernetes at scale.

What Is IT Infrastructure Monitoring?

IT infrastructure monitoring encompasses the tools, platforms, and practices used to continuously observe the health, performance, and availability of an organization’s technology stack. This includes servers, networks, storage, databases, containers, cloud services, and the applications that run on top of them. When something degrades or fails, monitoring systems generate alerts so engineering teams can respond before users are impacted.

Core capabilities typically include:

• Metrics collection – CPU, memory, disk I/O, network throughput, and custom application metrics
• Log aggregation – centralizing log data from diverse sources for search and analysis
• Alerting and on-call routing – notifying the right team at the right time
• Dashboards and visualization – giving operators a real-time view of system health
• Root-cause analysis – correlating events across layers to identify the origin of a problem
• Capacity planning – using historical trends to predict future resource needs

How COVID-19 Changed IT Monitoring Priorities

The pandemic forced organizations to rapidly extend their infrastructure in ways that exposed significant monitoring gaps. Several specific pressures emerged:

• Remote workforce scale-up – VPN concentrators, virtual desktop infrastructure (VDI), and collaboration platforms such as video conferencing tools experienced sudden, dramatic load increases. Teams needed real-time visibility into these systems to prevent bottlenecks.
• Cloud adoption acceleration – Organizations migrated workloads to public cloud providers faster than planned, creating hybrid environments that existing on-premises monitoring tools were not designed to cover.
• Security surface expansion – More endpoints and more distributed access patterns increased the importance of integrating security telemetry with performance monitoring.
• Reduced on-site staffing – With fewer engineers physically present in data centers, automated monitoring and alerting became mission-critical rather than aspirational.
• Budget constraints – Simultaneously, many organizations faced pressure to consolidate tooling and reduce licensing costs, favoring platforms that offered broad coverage rather than point solutions.

Key Vendors in the IT Infrastructure Monitoring Market

A range of established platforms and emerging players compete in this space. The following vendors are frequently cited among the market leaders:

Splunk

Splunk built its reputation on log aggregation and search, and has since expanded into full observability through its Observability Cloud product line (incorporating former acquisitions such as SignalFx). It serves large enterprises with complex, heterogeneous environments and is known for its powerful query language (SPL) and extensive ecosystem of integrations.

AppDynamics (Cisco)

AppDynamics focuses on application performance monitoring (APM) and business transaction visibility. Acquired by Cisco, it integrates network-level observability with application-layer insights, making it popular in enterprises that want to correlate infrastructure health with business outcomes.

Nagios Enterprises

Nagios is one of the longest-standing open-source monitoring frameworks. Nagios Core remains widely deployed in organizations that prefer self-hosted, highly customizable solutions. Nagios XI, the commercial offering, adds a web UI and support. Its plugin architecture means it can monitor virtually any device or service, though it requires more manual configuration than modern SaaS alternatives.

CA Technologies (Broadcom)

CA Technologies, now part of Broadcom, offers the DX Operations Intelligence suite (formerly CA Performance Management and related products). It targets large enterprises and managed service providers with complex network and application monitoring requirements.

ScienceLogic

ScienceLogic’s SL1 platform is designed for hybrid cloud and multi-tenant environments. It is particularly popular among managed service providers (MSPs) who need to monitor infrastructure across multiple customer accounts from a single pane of glass.

Spiceworks

Spiceworks offers free network monitoring and help desk tooling aimed at small and mid-sized businesses. While it lacks the depth of enterprise-grade platforms, its zero-cost model made it a popular entry point for organizations with limited budgets—a consideration that became more relevant during pandemic-era cost pressures.

Market Drivers Through 2027

Several structural forces continue to drive investment in IT infrastructure monitoring beyond the immediate pandemic period:

• Hybrid and multi-cloud architectures – As organizations distribute workloads across on-premises data centers, private clouds, and multiple public cloud providers, unified monitoring becomes harder and more necessary.
• Containerization and microservices – Kubernetes-based deployments create dynamic, ephemeral infrastructure that traditional agent-based monitoring struggles to track. This is fueling adoption of cloud-native observability tools.
• AIOps adoption – Machine learning-assisted anomaly detection and automated root-cause analysis are increasingly standard features, reducing alert fatigue and shortening mean time to resolution (MTTR).
• Compliance and regulatory requirements – Industries such as financial services, healthcare, and government have strict requirements around audit logging and availability reporting, supporting demand for enterprise monitoring platforms.
• Edge computing growth – IoT and edge deployments extend the monitoring perimeter far beyond the traditional data center, creating new tool requirements.

Market Challenges and Restraints

Despite strong tailwinds, the market faces real obstacles:

• Tool sprawl and integration complexity – Many organizations operate a patchwork of overlapping monitoring tools, and consolidating them without losing coverage is technically and organizationally difficult.
• Skills shortage – Effective use of advanced monitoring platforms requires expertise in both the tools themselves and the infrastructure being monitored. This expertise is in short supply.
• Data volume and cost – Modern distributed systems generate enormous quantities of telemetry. Storing and querying this data at scale is expensive, and controlling observability costs has become a discipline in its own right.
• Vendor lock-in concerns – Proprietary agents and data formats make switching monitoring vendors costly, leading some organizations to favor open standards such as OpenTelemetry.

Questions Engineering and Operations Teams Should Ask When Evaluating Platforms

When assessing IT infrastructure monitoring solutions, consider the following:

1. Does the platform support all infrastructure layers you need to monitor—bare metal, VMs, containers, serverless, and cloud-managed services?
2. What is the total cost of ownership, including data ingestion fees, agent licensing, and storage?
3. How does the tool handle dynamic infrastructure such as auto-scaling groups and ephemeral containers?
4. Does it support OpenTelemetry or other open standards to avoid vendor lock-in?
5. What AIOps or machine learning capabilities does it offer to reduce alert noise?
6. How does it integrate with your existing incident management, ticketing, and on-call tooling?
7. What are the compliance and data residency options, particularly for regulated industries?
8. What is the vendor’s roadmap for edge and IoT monitoring support?

Conclusion

COVID-19 acted as a forcing function that exposed infrastructure monitoring gaps and accelerated investment in observability across industries. The market continues to evolve rapidly, driven by cloud adoption, containerization, and the growing complexity of distributed systems. Organizations evaluating platforms should look beyond point-in-time feature comparisons and consider total cost, openness, and the vendor’s ability to support hybrid and multi-cloud environments over the long term. Established players like Splunk, AppDynamics, and Nagios continue to hold significant market share, but the space remains competitive with both commercial and open-source alternatives gaining traction.

Recognizes deep cloud-based expertise building digital mosaics across key verticals

Persistent Systems has achieved Amazon Web Services (AWS) Software-as-a-Service (SaaS) Competency status. This designation recognizes that Persistent has demonstrated deep experience helping organizations design and build SaaS and cloud-native solutions on AWS. Persistent is a global solutions company that excels at delivering digital business acceleration and enterprise modernization for organizations across industries such as banking/financials services, healthcare and life sciences, and the industrial sector.

Achieving the AWS SaaS Competency differentiates Persistent as an AWS Partner Network (APN) member that possess deep domain expertise in one or more of the following core SaaS categories – Design Services and Builders. APN Consulting Partners in the Design Services category have expertise in designing and implementing complex cloud-native SaaS solutions with AWS infrastructure. APN Consulting Partners in the Builders category have deep expertise in building cloud-native SaaS applications via software development. To receive the designation, APN Partners must possess deep AWS expertise in designing and building SaaS solutions seamlessly on AWS.

Recommended AI News: AiThority Interview With Al Sporer, Executive VP and General Manager at Anexinet

Christopher O’Connor, CEO & Executive Director, Persistent Systems
“Organizations around the globe are demanding effective cloud-based platforms to meet their specific needs. Persistent designs and delivers a composable digital mosaic across all of our industry and technology categories, accelerating time to value for our clients and increasing their business agility. We have developed a deep relationship with AWS, so becoming one of the first APN Partners to achieve this AWS SaaS Competency status in both Design Services and Builders categories is a testament to our commitment to help customers move to the cloud, leveraging the agility, breadth of services and innovation AWS provides.”
AWS is enabling scalable, flexible, and cost-effective solutions from startups to global enterprises. To support the seamless integration and deployment of these solutions, AWS established the AWS Competency Program to help customers identify Consulting and Technology APN Partners with deep industry experience and expertise.

Persistent SaaS Enablement on AWS

Persistent designs and builds SaaS enablement services on AWS to software vendors and enterprises to move
to cloud-based applications. This includes managing their deployment and lifecycle on the cloud and
transitioning to a subscription-based pricing so that enterprises can move customers to a pay-as-you-sell model,
accelerate time to value and outsource SLA responsibilities, all with only a small upfront investment.John
Kitchingman, Managing Director, Euronorth, Dassault Systèmes

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Quote from Philip Acton CEO of My Community Bank
“Leveraging the strength and reliability of AWS, Persistent and its partner Gojoko have built a fintech ecosystem,
giving My Community Bank access to a state-of-the-art and end-to-end banking infrastructure. Seamlessly
integrated technologies from partners like Mambu and Salesforce running on AWS has helped My Community
Bank become the fastest growing credit union in the UK, delivering an enhanced product to our customers.”

Realizing these outcomes requires more than reorganizing teams or adopting an agile mindset. The underlying network infrastructure must be purpose-built—or modernized—to support the speed, automation, and flexibility that DevOps demands. Without the right network foundation, even the most disciplined DevOps teams will hit friction that slows deployments and limits reliability.

This article examines the five essential capabilities your network infrastructure must provide to genuinely support DevOps practices.

1. Automation

Manual configuration of network components—DNS records, firewall rules, IP address assignments—is slow, error-prone, and impossible to scale at DevOps velocity. Automation addresses all three problems simultaneously.

The most effective approach is Infrastructure as Code (IaC), where network resources are defined, provisioned, and modified through code rather than manual processes or GUI-driven workflows. Tools like Terraform let teams declare the desired state of their infrastructure and apply changes consistently across environments. Key advantages include:

• Repeatability: The same configuration can be applied identically across development, test, staging, and production environments, eliminating the classic “it worked on my machine” problem.
• Version control: Infrastructure definitions stored in Git provide a complete audit trail—who changed what, when, and why—and allow fast rollback to a known-good state.
• Standardization: Automated configuration enforces consistent standards, reducing configuration drift between environments.

Network infrastructure that exposes a well-documented API is a prerequisite for IaC adoption. Vendors and platforms that require manual appliance configuration are fundamentally incompatible with modern DevOps workflows.

2. CI/CD Pipeline Integration

Continuous Integration (CI) is the practice of merging code changes into a shared repository frequently, with each merge triggering automated builds and tests. Continuous Delivery (CD) extends this by automatically deploying validated builds to one or more environments—development, test, staging, or production.

Popular CI/CD tools used by DevOps teams include Jenkins, GitLab CI, CircleCI, and Ansible, among others. For the network layer to support these pipelines effectively, it must:

• Expose APIs that CI/CD tools can call to provision or update network resources as part of a pipeline run.
• Support automated provisioning of test and staging environments without requiring manual network team intervention.
• Integrate with infrastructure orchestration tools so that network changes are treated as first-class citizens in the deployment pipeline—tested, reviewed, and deployed with the same rigor as application code.

When network provisioning is outside the CI/CD pipeline—handled separately by a network ops team on a different cadence—it becomes a bottleneck. Every manual handoff adds delay and introduces risk.

3. Elasticity

DevOps workflows involve constantly spinning environments up and tearing them down. A developer might need an isolated network environment for an afternoon to test a new feature. A QA team might need multiple parallel environments running simultaneously for different test suites. A production system might need to scale out rapidly in response to a traffic spike, then contract again when demand drops.

Traditional appliance-based network infrastructure is poorly suited to this model for several reasons:

• Artificial capacity limits: Physical and virtual appliances are often licensed or architected with hard limits on transactions per second, concurrent sessions, or lease rates—limits that may not align with actual demand patterns.
• Long procurement cycles: When capacity runs out, procuring additional appliances takes weeks or months. By the time new hardware arrives, the opportunity may have passed.
• Inflexible scaling: Hardware-based solutions scale in large, expensive increments rather than in proportion to actual need.

DevOps-friendly network infrastructure must be capable of scaling dynamically—both up and down—in response to demand, without manual intervention and without arbitrary capacity ceilings. Software-defined and cloud-native network services are generally far better positioned to meet this requirement than traditional appliance-based approaches.

4. Container and Kubernetes Compatibility

Containers have become the standard packaging format for cloud-native applications. A container bundles application code together with its dependencies—libraries, binaries, and configuration files—into a portable, lightweight unit that runs consistently across different environments, from a developer’s laptop to a public cloud.

Containers are particularly well-suited to microservices architectures, where an application is decomposed into small, independently deployable services that each perform a single function. Compared to monolithic three-tier architectures, microservices are easier to scale, easier to update, and easier to reason about—but they generate significantly more complex network traffic patterns, with many services communicating with many others.

Network infrastructure must be capable of supporting containerized workloads specifically. This includes:

• Dynamic DNS management: Containers are ephemeral; their IP addresses change frequently. DNS must update automatically to reflect the current state of running containers.
• IP address management (IPAM): Container orchestration platforms assign and release IP addresses at high frequency. IPAM systems must handle this dynamism without manual intervention.
• Intelligent traffic routing and load balancing: Traffic must be routed reliably between containers, often across nodes or availability zones, with load balanced distribution for performance and resilience.
• Kubernetes integration: Kubernetes is the dominant container orchestration platform. Network infrastructure that cannot integrate with Kubernetes—through CNI plugins, native API support, or other integration points—will create operational friction and limit the platform’s capabilities.

When evaluating network vendors, explicitly ask about container and Kubernetes compatibility. Many legacy appliance-based solutions were designed before containers were prevalent and cannot support the dynamic, ephemeral nature of containerized workloads without significant workarounds.

5. Self-Service Access with Role-Based Controls

One of the most common sources of friction in DevOps workflows is the dependency on a separate network operations team to provision infrastructure for development and testing. When a developer needs a DNS entry, a new VLAN, or a load balancer configuration, submitting a ticket to a network team and waiting for fulfillment breaks the flow of work and slows delivery.

Self-service network infrastructure allows development teams to provision the network resources they need—within defined boundaries—without requiring intervention from network operations. This typically involves:

• A self-service portal or API that developers can use to request and configure network resources on demand.
• Role-based access controls (RBAC) that define what each team or individual is permitted to do. Developers might be able to manage DNS and DHCP for their own namespaces, while firewall rule changes for production networks still require elevated approval.
• Guardrails and policy enforcement that prevent self-service actions from violating security policies or creating conflicts with other environments.

Done correctly, self-service access speeds up application development and deployment, reduces the ticket queue burden on network operations teams, and frees network engineers to focus on strategic infrastructure work rather than routine provisioning tasks.

Putting It Together

DevOps aims to remove friction from the software delivery process by giving development teams greater autonomy and by unifying development and operations around shared goals. But that autonomy is only sustainable when the underlying infrastructure—including the network—is built to support it.

The five capabilities described here—automation, CI/CD integration, elasticity, container and Kubernetes support, and self-service access with appropriate controls—are not optional enhancements for mature DevOps organizations. They are foundational requirements. Organizations that address these infrastructure needs alongside cultural and process changes will see faster delivery cycles, higher-quality software, and reduced operational overhead throughout the application lifecycle.

When assessing your current network infrastructure against these requirements, be honest about gaps. Legacy appliance-based solutions often fail on multiple dimensions simultaneously—limited APIs block automation, rigid capacity models prevent elasticity, and lack of container awareness creates blind spots in modern application deployments. Addressing these gaps proactively is a prerequisite for sustained DevOps success.

The Case for Growth Stocks in a Volatile Market

When equity markets become volatile, investors face a recurring dilemma: retreat into large-cap dividend payers with proven balance sheets, or stay committed to high-growth companies that can deliver outsized long-term returns? The argument for growth stocks is straightforward — while they experience sharper drawdowns during downturns, they have historically created substantial wealth for patient investors.

In the observability and data monitoring space, Splunk (NASDAQ: SPLK) had long been considered the benchmark. Its enterprise platform for big data search, monitoring, and analytics became a standard tool in IT operations. But by early 2020, a newer entrant — Datadog (NASDAQ: DDOG) — was growing faster and building a more modern, cloud-native product portfolio.

What Does Datadog Actually Do?

Datadog is a SaaS-based monitoring and analytics platform designed for modern, cloud-scale infrastructure. Unlike legacy monitoring tools, it was built from the ground up to handle dynamic, containerised, and microservices-based architectures. Its platform integrates three historically separate disciplines into a single unified interface:

• Infrastructure Monitoring — visibility into hosts, containers, serverless functions, and cloud services across AWS, Azure, GCP, and hybrid environments.
• Application Performance Monitoring (APM) — distributed tracing and service dependency mapping for modern applications.
• Log Management — centralised log ingestion, indexing, and analysis correlated with metrics and traces.

Datadog’s product timeline reflects a deliberate expansion strategy:

• 2012: Infrastructure monitoring platform launched to address cloud-native architectures.
• 2017: APM product introduced, enabling end-to-end request tracing.
• 2018: Log management added to close the observability loop.
• 2019: User experience monitoring, network performance monitoring, and security monitoring joined the platform.

The result is an end-to-end observability platform that is cloud-agnostic — meaning it works consistently across multi-cloud and hybrid environments without locking customers into a single provider.

Datadog’s Addressable Market

Datadog’s platform targets the IT operations management market. According to Gartner estimates cited at the time of the original article, this market represented a $37 billion addressable opportunity. With annual revenue of approximately $363 million in 2019, Datadog held less than 1% of that market — leaving substantial runway for growth over the following decade.

Key Growth Metrics (FY2019)

Datadog’s financial and operational metrics for 2019 painted a picture of rapid, compounding growth:

• Revenue growth: 85% year-over-year in Q4 2019.
• Total customers: 10,500 at end of 2019, up from 7,700 in 2018.
• Enterprise customers (ARR > $100K): 858, representing 89% year-over-year growth.
• Average enterprise ARR: Grew from $160,000 (2018) to $230,000 (2019).
• Average mid-market ARR: Grew from $110,000 (2018) to $170,000 (2019).
• Net retention rate: 130%, driven by customers adopting additional products.
• Multi-product adoption: Over 60% of customers using two or more products.
• Operating cash flow: $24.2 million in 2019, up from $10.8 million in 2018.
• Cash balance: $778 million at year-end, providing strong liquidity.

The 130% net retention rate is particularly significant from a product perspective. It means that, on average, existing customers were spending 30% more year-over-year — a sign that Datadog’s platform strategy of cross-selling additional modules was working in practice.

2020 Guidance (as of April 2020)

At the time of the original article, Datadog had issued the following guidance:

• Q1 2020: Revenue of $117–119 million, implying approximately 68.5% year-over-year growth at the midpoint.
• Full-year 2020: Revenue of $535–545 million, implying approximately 49% year-over-year growth.

These projections carried a caveat: the COVID-19 pandemic introduced significant uncertainty around enterprise IT spending. The article acknowledged that guidance could be revised downward as the macroeconomic picture became clearer.

Datadog vs. Splunk: Valuation and Growth Trade-Off

At the time of writing (April 2020), Datadog traded at a market-cap-to-forward-sales ratio of approximately 22.6x, compared to Splunk’s 8.4x. The premium valuation reflected Datadog’s significantly higher revenue growth rate.

Splunk remained a well-established, valuable platform — particularly for security information and event management (SIEM) and enterprise log analytics. However, the argument presented was not that Splunk was a poor business, but that Datadog’s growth trajectory, cloud-native architecture, and expanding product footprint made it a more compelling growth story at that stage of its development.

Why the Platform Approach Matters Technically

From an engineering perspective, Datadog’s integrated platform addresses a real operational pain point: tool sprawl. Historically, teams used separate tools for metrics, logs, and traces — each with its own interface, query language, and data model. Correlating an application slowdown across these three pillars required manual effort and context-switching.

Datadog’s unified data model allows engineers to move seamlessly between a host-level metric anomaly, the associated application traces, and the underlying logs — all within a single platform. This reduces mean time to resolution (MTTR) and lowers the operational burden on SRE and DevOps teams.

Its cloud-agnostic integrations — covering over 450 technologies at the time — also meant that organisations running workloads across AWS, Azure, and GCP did not need to deploy separate monitoring stacks per cloud.

Important Caveats for Readers

This article summarises analysis from April 2020. The financial figures, valuations, customer counts, and competitive dynamics described here are historical. Datadog, Splunk, and the broader observability market have evolved considerably since then. This content should not be interpreted as current investment advice. Always verify figures against the most recent company filings and consult a qualified financial adviser before making investment decisions.

The Hybrid and Multi-Cloud Reality for Public Sector Organizations

Cloud computing has become the default platform for digital innovation, and a growing number of organizations — including government departments and public-sector bodies — are deploying some form of hybrid cloud or multi-cloud architecture. However, cloud adoption in the public sector comes with a distinct set of challenges that go beyond typical enterprise concerns.

Chief among these challenges are:

• Organizational silos that prevent cohesive IT strategy across departments
• Risk-averse institutional culture that slows adoption of new infrastructure models
• Rising IT complexity introduced by managing multiple cloud providers and on-premises environments simultaneously
• Secure application delivery requirements that must meet regulatory and compliance standards specific to government operations

These factors together create friction that can prevent public-sector organizations from realizing the full efficiency and citizen-service improvements that cloud infrastructure promises.

The Core Problem: Inconsistent Networking and Security Across Clouds

One of the most significant operational pain points in multi-cloud environments is that networking and security are implemented differently by each cloud provider. AWS, Azure, Google Cloud, and private data centers each have their own native tools, APIs, and policy frameworks. When organizations try to enforce consistent security policies across these environments, the result is often:

• Higher management overhead and operational cost
• Reduced visibility into network traffic and security events
• Increased risk of misconfiguration due to platform-specific tooling
• Slower response to threats because there is no unified control plane

For government organizations in particular — which must maintain service continuity even during emergencies — this fragmentation represents a serious risk to both security posture and operational resilience.

Breaking Down Network Silos: The Case for Consistent Policy Across Clouds

The solution discussed at the webinar centers on replacing siloed, per-cloud networking infrastructure with a unified approach that delivers consistent networking and security for applications regardless of where they run — on-premises, in a public cloud, or across multiple clouds.

Rather than managing separate firewall rules, network segments, and access policies for each environment, IT teams can benefit from:

• Global security policies that apply uniformly across virtual networks, cloud regions, and providers
• Precise micro-segmentation to control lateral movement within and between cloud environments
• Centralized visibility and control over network traffic without requiring agents or hardware at every perimeter
• Support for containerized and microservices-based workloads, which have unique east-west traffic patterns that traditional perimeter security cannot adequately address

This approach is particularly important during periods of disruption — such as a public health emergency — when IT teams must rapidly scale services or enable remote work while maintaining security guarantees.

VMware’s Approach: NSX Cloud, NSX Data Center, and Cloud Foundation

At the April 2020 webinar, Vivek Lodha, Senior Network and Security Specialist at VMware India, presented VMware’s portfolio of solutions aimed at addressing these challenges for government organizations accelerating hybrid cloud adoption.

VMware NSX Data Center

VMware NSX Data Center provides network virtualization and security for the software-defined data center. It enables IT administrators to programmatically create, snapshot, delete, and restore complex network topologies in seconds. Key capabilities relevant to public-sector use cases include micro-segmentation for zero-trust security inside the data center and policy-based automation for network provisioning.

VMware NSX Cloud

VMware NSX Cloud extends consistent networking and security policy to workloads running natively in public clouds. Instead of reconfiguring security rules each time a workload moves to a new cloud environment, NSX Cloud enforces the same policy framework across AWS and Azure (and potentially other supported clouds), reducing both complexity and the chance of policy gaps that attackers could exploit.

VMware Cloud Foundation

VMware Cloud Foundation (VCF) integrates compute, storage, networking, and cloud management into a unified hybrid cloud platform. For public-sector organizations, VCF can provide the standardized infrastructure foundation needed to run both traditional enterprise applications and modern container-based workloads with consistent operational tooling.

Why This Matters for Government IT Leaders

Government IT infrastructure must deliver citizen-centric services reliably, securely, and at scale. As Ankush Kumar, Consulting Editor at Tech Observer, highlighted during the webinar’s opening, agility in government IT is not merely a technology objective — it directly affects the quality and speed of public services delivered to citizens.

For IT leaders in the public sector, the practical takeaway is this: adopting a consistent networking and security framework is not an optional optimization. It is a prerequisite for:

• Achieving meaningful business continuity during crises
• Controlling operational expenditure as cloud footprints grow
• Meeting compliance and audit requirements across multi-cloud deployments
• Enabling development teams to adopt microservices and containers without creating ungoverned network paths

Key Takeaways

• Hybrid and multi-cloud adoption in the public sector is accelerating, but inconsistent networking and security across clouds adds cost and risk.
• Breaking down administrative network silos requires a platform that enforces consistent policy across on-premises data centers and public clouds.
• VMware NSX Data Center, NSX Cloud, and Cloud Foundation together offer a path toward unified network visibility and security for government workloads.
• Container and microservices environments require security models that go beyond traditional perimeter-based approaches.
• A consistent networking and security architecture is foundational to business continuity planning, not a post-adoption concern.

This article is based on a summary of a webinar held on April 17, 2020. For the most current VMware product capabilities and version details, refer to VMware’s official documentation.