What is AWS?

The AWS is a complete, developing and cloud computing platform that is delivered by Amazon that includes a mixture of substructure as a service (IaaS), a platform as a service (PaaS) and enfolded as a package as a service (SaaS) contributions. The AWS services will provides a proposal to a goggle tools like figure power, info storage and content delivery services.

The AWS (amazon web services) provides many varieties of various tools and solutions for creativities and package developers that might be utilized in information centres in countries. The teams like administration agencies, the education organizations, non-profits and personal organizations etc. may also use the AWS services.

How AWS works?

The AWS is alienated into dissimilar services as it is designed in various ways that supported the user’s needs. The Users ought to be gifted to piece the alternatives associated with distinct server maps for an AWS service.

But many services comprise to the Amazon net Services assortment which has those for cipher, databases, infrastructure running and conjointly for the applying development and security. These services, by categorized as follows:-

• For Computing.

• For storing databases.

• For the information management.

• For Migration.

• For Hybrid cloud.

• For Networking.

• For Development tools.

• For Management.

• For watching.

• For the safety.

• For the Governance.

• For huge knowledge management.

• For the Analytics.

• For computing (AI).

• For Mobile development.

• For Messages and notification.

What are the importance of AWS?

The AWS courses provide immersive knowledges of which inspires learners to smartly assume, question, and information conclusions. Different the many PowerPoint displays and room recordings, the AWS courses embrace animated graphics, audio tale, video footage, and communication components that build even the foremost multifarious topics each engaging and simple to know. This course additionally covers the way to produce unexperienced buildings and build them into strong and adjusted solutions.

• It manages multiple AWS accounts for your organization.

• It Connect on-premises datacentre to AWS cloud.

• It Discuss asking insinuations of connecting multi-region VPCs.

• It Moves giant information from on-premises datacentre to AWS.

• It styles an outsized information stores for AWS cloud.

• It perceive totally different field styles for climb an outsized web site.

• It shield your setup from DDoS attack.

• It secures your information on AWS with encoding.

• It styles protection of data-at-rest additionally as data-in-flight.

• It enhances the performance of your solutions.

Why should we learn AWS?

The AWS provides the best level of information privacy and security to its customers in spite of their business size. It does not matter whether or not you’re running a tiny low start-up or an outsized business AWS has in depth security support that offers period of time insight on suspicious activity and potential vulnerabilities.

It allows organizations to achieve further coaching and resources (technical, business, sales, and marketing) which will higher serve their customers exploitation AWS services. A lot of certifications a corporation has, the upper the advantages they will access through the layer AWS Partner Network.

Learning these core services can provide you with an honest understanding of however AWS approaches security, networking, knowledge storage, and cypher capability.

Presently, cloud computing has taken the technical world to a brand new level. Cloud computing additionally provides safe access to the servers, storage, databases and offers plenty of application services over the web.

What are the benefits of learning AWS?

The next discussion would set up 10 dissimilar AWS certification blessings. This feature of the discussion would build the inspiration for telling the positive consequences connected with AN AWS certification communication. The advantage of AWS is that companies solely have to be compelled to pay money for the services that they use. The AWS services facilitate businesses to make complete substructure. The total application surroundings facilitate businesses to run applications while not the requirement for physical hardware. As a result, businesses may be ready to cut back their incidentals aboard guaranteeing the measurability of processes. The advantage of AWS is that companies solely have to be compelled to pay money for the services that they use. The AWS services facilitate businesses to make whole infrastructure. The total application environments facilitate businesses to run displays while not the requirement for physical hardware. As a result, companies may be ready to decrease their prices besides safeguarding the measurability of operations.

• For the Preparation for the longer term.

• For the new streets for developing skilled experience.

• To Gain AN appreciation for your labours.

• To show your commitment levels.

• To restricted difficulties to clear AWS certification.

• For a more robust pay check.

• A possibilities to expand skilled network.

• The Employers conjointly get advantages.

Conclusion:-

So here we are! As of now we had come to an end of this article, hope you all will be satisfied with this article and you would be clear about “why I should learn AWS?” The AWS is a complete, developing and cloud computing platform that is delivered by Amazon that includes a mixture of substructure as a service (IaaS), a platform as a service (PaaS) and enfolded as a package as a service (SaaS) contributions. The AWS services will provides a proposal to a goggle tools like figure power, info storage and content delivery services.

If you want to learn AWS then DevOpsSchool is one the top institute for AWS Certification and become a AWS Certified Professional.

Thank You!

Cloud Computing Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis. It also provides market information in terms of development and its capacities.

Some of the Top companies Influencing in this Market includes:

PT Cyberindo Mega Persada (CBNCloud), Microsoft, Amazon Web Services, Telkom Indonesia, AGIT, VMWare, Infinys System Indonesia, Oracle, Joyent Cloud

Various factors are responsible for the market’s growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global Cloud Computing market. It also gauges the bargaining power of suppliers and buyers, threat from new entrants and product substitute, and the degree of competition prevailing in the market. The influence of the latest government guidelines is also analyzed in detail in the report. It studies the Cloud Computing market’s trajectory between forecast periods.

Global Cloud Computing Market research report offers:

Market definition of the global Cloud Computing market along with the analysis of different influencing factors like drivers, restraints, and opportunities.
Extensive research on the competitive landscape of global Cloud Computing
Identification and analysis of micro and macro factors that are and will effect on the growth of the market.
A comprehensive list of key market players operating in the global Cloud Computing market.
Analysis of the different market segments such as type, size, applications, and end-users.
It offers a descriptive analysis of demand-supply chaining in the global Cloud Computing market.
Statistical analysis of some significant economics facts
Figures, charts, graphs, pictures to describe the market clearly.

Regions Covered in the Global Cloud Computing Market Report 2021:
• The Middle East and Africa (GCC Countries and Egypt)
• North America (the United States, Mexico, and Canada)
• South America (Brazil etc.)
• Europe (Turkey, Germany, Russia UK, Italy, France, etc.)
• Asia-Pacific (Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)

The cost analysis of the Global Cloud Computing Market has been performed while keeping in view manufacturing expenses, labor cost, and raw materials and their market concentration rate, suppliers, and price trend. Other factors such as Supply chain, downstream buyers, and sourcing strategy have been assessed to provide a complete and in-depth view of the market. Buyers of the report will also be exposed to a study on market positioning with factors such as target client, brand strategy, and price strategy taken into consideration.

What will be the market size and the growth rate by the end of the forecast period?
What are the key Cloud Computing Market trends impacting the growth of the market?
What are the potential growth opportunities and threats faced by the leading competitors in the market?
What are the key outcomes of Porter’s five forces analysis and the SWOT analysis of the key players functioning in the global Cloud Computing Market?
This report gives all the information regarding industry Overview, analysis and revenue of this market.
What are the market opportunities and threats faced by the vendors in the global Cloud Computing market?
Table of Content (TOC)

Global Cloud Computing Market Report 2021 – Growth, Trend and Forecast to 2027
Chapter 1 Cloud Computing Market Overview
Chapter 2 Global Economic Impact on Cloud Computing Industry
Chapter 3 Global Cloud Computing Market Competition by Manufacturers
Chapter 4 Global Production, Revenue (Value) by Region (2014-2021)
Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions (2014-2021)
Chapter 6 Global Production, Revenue (Value), Price Trend by Type
Chapter 7 Global Market Analysis by Application
Chapter 8 Manufacturing Cost Analysis
Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 10 Marketing Strategy Analysis, Distributors/Traders
Chapter 11 Market Effect Factors Analysis
Chapter 12 Global Cloud Computing Market Forecast (2021-2027)
Chapter 13 Appendix

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Introduction: Global Web Service Cloud Market, 2020-25
The global Web Service Cloud market report is analyzed based on its market share by value and volume. A regional, country, and global level analysis of all the Web Service Cloud segments is done in the report. The study includes all major geographic regions across the world influencing the Web Service Cloud market. Key insights of the Web Service Cloud market are dominating factors, prospect growth opportunities, restraints, and challenges are given in the report. The finding of the report is supported by porter’s five forces analysis, competitiveness analysis, assessment of key characteristics of the competitive landscape, and raw material analysis. The research methodologies and the resultant data provided in the report match your business needs.

Competition Assessment: Global Web Service Cloud Market

Atlantic
Amazon Web Services
Google Cloud Platform
IBM Cloud
Rackspace
VMware
DigitalOcean
Oracle Cloud
CloudSigma

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The investment research data offered in the report allows the stakeholders and investors in the Web Service Cloud market to focus on the ongoing and upcoming investment opportunities and makes them aware of the investment scenarios across the Web Service Cloud market. The strategic intelligence capabilities boost your business extension and help better understand the potential of diverse industries in the Web Service Cloud market. Qualitative and quantitative analysis of Web Service Cloud market scenarios as per the geographical regions and performance of its different regions is provided in the report. The research analysis study is customized according to the business requirements of the market participants. Furthermore, the report highlights specifications and challenges, including multiple methodologies to extract accurate data figures and facts, detailed interviews, and studies the competitive landscape of the Web Service Cloud market

Segmentation by Type:

Infrastructure As A Service
Platform As A Service
Software As A Service

Based on application, segmentation is primarily been harnessed to understand the scope of application based on industrial demands and user preferences.

Segmentation by Application:

SMEs
Large Enterprises

Regional Segmentation identifies Americas, European nations, APAC countries and MEA as popular growth hubs.

The report highlights various aspects in the Web Service Cloud market and answers relevant questions on the Web Service Cloud market:
1. What are the best investment options to launch new products and offer advanced services in the Web Service Cloud market?
2. What are the value propositions relevant to the customer or market segment a business should focus on while making new research or investing funds in the Web Service Cloud market?
3. What policy changes will help stakeholders to boost their supply chain and demand network?
4. Which regions would demand more products and services offered in certain segments in the forecast period?
5. What are the strategies that have helped the established players to reduce supplier costs, procurement and, logistics costs?
6.C-suite perspective leveraged to move businesses to new growth trajectory?
7. What are the government interventions boosting the Web Service Cloud market or government regulations that may challenge the status of the regional and global industries in the Web Service Cloud market?
8. How will the political and economic crisis affect opportunities in Web Service Cloud growth areas?

Table of Contents
Chapter One: Report Overview
1.1 Study Scope
1.2 Key Market Segments
1.3 Players Covered: Ranking by Web Service Cloud Revenue
1.4 Market Analysis by Type
1.4.1 Global Web Service Cloud Market Size Growth Rate by Type: 2020 VS 2026
1.5 Market by Application
1.5.1 Global Web Service Cloud Market Share by Application: 2020 VS 2026
1.6 Study Objectives
1.7 Years Considered

Chapter Two: Global Growth Trends by Regions
2.1 Web Service Cloud Market Perspective (2015-2026)
2.2 Web Service Cloud Growth Trends by Regions
2.2.1 Web Service Cloud Market Size by Regions: 2015 VS 2020 VS 2026
2.2.2 Web Service Cloud Historic Market Share by Regions (2015-2020)
2.2.3 Web Service Cloud Forecasted Market Size by Regions (2021-2026)
2.3 Industry Trends and Growth Strategy
2.3.1 Market Top Trends
2.3.2 Market Drivers
2.3.3 Market Challenges
2.3.4 Porter’s Five Forces Analysis
2.3.5 Web Service Cloud Market Growth Strategy
2.3.6 Primary Interviews with Key Web Service Cloud Players (Opinion Leaders)

Chapter Three: Competition Landscape by Key Players
3.1 Global Top Web Service Cloud Players by Market Size
3.1.1 Global Top Web Service Cloud Players by Revenue (2015-2020)
3.1.2 Global Web Service Cloud Revenue Market Share by Players (2015-2020)
3.1.3 Global Web Service Cloud Market Share by Company Type (Tier 1, Tier Chapter Two: and Tier 3)
3.2 Global Web Service Cloud Market Concentration Ratio
3.2.1 Global Web Service Cloud Market Concentration Ratio (CRChapter Five: and HHI)
3.2.2 Global Top Chapter Ten: and Top 5 Companies by Web Service Cloud Revenue in 2020
3.3 Web Service Cloud Key Players Head office and Area Served
3.4 Key Players Web Service Cloud Product Solution and Service
3.5 Date of Enter into Web Service Cloud Market
3.6 Mergers & Acquisitions, Expansion Plans

With unfailing market gauging skills, has been excelling in curating tailored business intelligence data across industry verticals. Constantly thriving to expand our skill development, our strength lies in dedicated intellectuals with dynamic problem solving intent, ever willing to mold boundaries to scale heights in market interpretation.

The latest report addition addressing stark alteration s in global Cloud Computing Stack Layers market is a must-have business document to aid various investment discretion of leading players and aspiring novice entrants in the industrial space.

The research documentation on global Cloud Computing Stack Layers market offers readers new perspectives to decode market developments highlighting crucial factors such as market size and dimensions, along with trend identification and competition assessment affecting market growth projections across geographies. An assessment guide of global Cloud Computing Stack Layers market with in-depth references of drivers, novel growth opportunities, existing barrier analysis have also been well represented in this versatile report to recoup growth track.

Competition Assessment: Global Cloud Computing Stack Layers Market

Amazon Web Services
Salesforce
Microsoft Azure
IBM
Google Cloud Platform
SAP
Rackspace
H&P Helion
OVH
Avaya
Oracle

The competition assessment of the global Cloud Computing Stack Layers market is a requisite tool to understand various vendor activities and their preferred mode of business delivery with promising business outcome have been aggressively discussed in this section of the report. Further, other relevant details such as SWOT assessment of each of the mentioned players, pricing strategies braced by vendors aligning with end-user affordability as well as consumption and production dynamics along with facility expansion needs are also widely discussed in the report. The competition intensity of the vendor landscape, highlighting players and other relevant market participants are also assessed in the report besides evaluating the vendor potential on pre-set parameters and SWOT assessment.

Further, each of the products and services highlighted in the report have also been classified into specific segments to understand pricing strategies of each segment, revenue generation potential and overall sales performance throughout the assessment span.

Segmentation by Type:

Software as a Service (SaaS)
Platform as a Service (PaaS)
Infrastructure as a Service (IaaS)

Based on application, segmentation is primarily been harnessed to understand the scope of application based on industrial demands and user preferences.

Segmentation by Application:

Commercial Use
Public Services
Others

Regional Segmentation identifies Americas, European nations, APAC countries and MEA as popular growth hubs.

Key Questions Answered in the Report?
• This report is a dependable guide to understand core factors influencing growth and expansion in global Cloud Computing Stack Layers market
• The report helps readers to understand the most crucial factor instrumenting high revenue generation amidst staggering cut-throat competition.
• The report sheds ample light on regional scope and advances in geographical sphere that leverages highest revenue gains. Various factors such as profit margins, sales performance, past and current events that accelerate future scope of actions have also been amply discussed in the report aligning with customer expectations for end-to-end assessment.
• The report also includes versatile understanding of market developments across past and present timelines that eventually enunciate future plausibility of growth and uniform expansion.

COVID-19 Impact Analysis
This intensively researched report presentation has been prepared in real time parlance, rendering substantial attention towards COVID-19 outbreak that has lately wreaked unprecedented damage across industries, stagnating growth.

These crucial market relevant information are estimated to trigger tremendous growth returns and sustain market stability through the forecast tenure.

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Global Personal Cloud market research report is a highly intelligent reference guide that is equipped with meticulous research output to derive accurate and optimum understanding on market growth synopsis in terms of value and volume. The report is a top-down assessment of all major touchpoints on the market and includes an elaborate vendor profile comprising emerging competitors as well as dominant market players aiming for lucrative break in the competition domain. Internationally acknowledged research practices such as SWOT analysis and PESTEL analysis have also been roped in the report with relevant details on revenue generation trends as well as technological innovations witnessing ample investments.

Competitor Profiling: Global Personal Cloud Market

Apple
Microsoft
Google
Amazon Web Services
Seagate
Dropbox
Egnyte
Buffalo Technology
Sugarsync
Box
In the competition spectrum section of this report, readers are offered decisive understanding on frontline players and the overall competition intensity concerning the market has also been highly discussed in the report. Details pertaining to market share, revenue generation tendencies, product innovations as well as corporate collaborations such as M&A developments have been highly discussed in the report.

The report is also highlighting versatile details on the impact of COVID-19 implications on growth progression. Owing to devastating outcome of the global pandemic, this research report has incorporated pandemic management details in the report as well to encourage appropriate business decisions in global Personal Cloud market.

This report on global Personal Cloud market also extends versatile details on various elements such as product type and applications. Additional details on geographical expanse and regional diversification have also been highlighted in the report. With these information attributes, inquisitive market participants can derive logical insights, crucial to deliver growth specific investment decisions.

Analysis by Type: This section of the report includes factual details pertaining to the most lucrative segment harnessing revenue maximization.

Direct Revenues
Indirect Revenues
Analysis by Application: Further in the subsequent sections of the report, research analysts have rendered precise judgement regarding the various applications that the Personal Cloud market mediates for superlative end-user benefits.

Individual
Small Enterprises
Medium Enterprises

Global Personal Cloud Market Report Indicators: Points to Consider
 Competition Analysis: A critical analytical assessment of frontline players and a detailed overview of the entire competition gamut has been one of the prime focal points of this research study. A critical evaluation inclusive also of a detailed SWOT analysis of the mentioned players has been included in the report for the superlative business outcomes. The Global Personal Cloud Market report includes detailed assessment of revenue generation trends, capacity milestones, production and consumption patterns as well as gross margin and sales developments have been intricately discussed in this report for high end returns.

 Regional and Global Market Outlook: The report in its subsequent sections also encompasses veritable insights on region specific market performance. Further details in the report shed light on prominent growth hubs as well as vendor manufacturing and promotional activities across countries and regions that influence customer response. Details about import and export performance as well as production and consumption trends have also been discussed at length in the report.

 Market Segmentation: The report in its subsequent sections also elaborates on various segment-wise performance in catapulting high revenue growth. Each of the segment specified in the report allows readers to identify the highest potential market segment triggering revenue maximization.

CCV Partners with AWS Academy to Expand Cloud Education

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

What AWS Academy Provides

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

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

The Course: Fundamentals of Cloud Computing

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

Key details about the course include:

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

Why This Matters for Students and Employers

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

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

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

Nicole Stetson, Associate Academic Dean, Community College of Vermont

About the Community College of Vermont

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

Getting Started with AWS Certifications

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

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

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

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

What Is Build Automation?

Build automation is the practice of scripting or tooling the end-to-end process of turning source code into a shippable artifact. At minimum this covers three steps:

• Compilation – converting human-readable source code into binary or bytecode.
• Packaging – bundling compiled output, assets, and dependencies into a distributable format (JAR, Docker image, APK, etc.).
• Automated testing – running unit, integration, and smoke tests as part of every build so regressions are caught immediately.

Build automation tools eliminate the manual, error-prone steps that slow teams down and make releases unpredictable. When wired into a continuous integration (CI) pipeline, every code commit can trigger a full build-and-test cycle automatically, giving developers rapid feedback.

Two Fundamental Tool Architectures

Build tools generally fall into one of two design philosophies:

• Task-oriented tools – You define a dependency graph of tasks (e.g., compile → test → package). The tool resolves the graph and executes tasks in the correct order. Examples: Apache Ant, GNU Make, Gradle (task mode), Rake.
• Product-oriented tools – You declare the artifacts you want and the tool figures out what must be built or rebuilt to produce them. Examples: Bazel, Buck, Pants. These scale well for large monorepos because they only rebuild what has actually changed.

In practice, most modern CI platforms combine both approaches: a product-oriented build system handles compilation and testing, while a task-oriented pipeline orchestrator (Jenkins declarative pipelines, GitHub Actions workflows, GitLab CI YAML) coordinates the higher-level delivery stages.

How Build Automation Fits Into the Broader DevOps Toolchain

Build automation does not live in isolation. It is the engine at the centre of the modern software-delivery loop:

• Source control (GitHub, GitLab, Bitbucket) triggers builds on every push or pull-request.
• CI servers (Jenkins, CircleCI, CloudBees CI) orchestrate build, test, and artifact-publication steps.
• Artifact repositories (JFrog Artifactory, Sonatype Nexus, AWS CodeArtifact) store versioned build outputs.
• CD and release tools (Octopus Deploy, Spinnaker, Argo CD) pick up artifacts and deploy them to target environments.
• Mobile CI platforms (Bitrise, Fastlane) add platform-specific signing, provisioning, and app-store publishing steps.

This end-to-end chain is what DevOps practitioners mean by a CI/CD pipeline. Build automation is the first — and most foundational — link.

Key Vendors in the Build Automation Space

The AMA report identifies the following companies as significant participants in the global build automation software market. The list spans open-source foundations, cloud hyperscalers, SaaS CI/CD platforms, and IDE vendors:

Note: Vendor categorisation, product names, and ownership structures can change. Verify current offerings directly with each vendor.

Market Drivers

Several forces are pushing adoption of build automation tooling across industries:

• Accelerating release cadences. Organisations moving from quarterly releases to continuous delivery need automated, repeatable builds that cannot depend on a single person’s local machine.
• Growth of mobile and IoT development. The explosion of Android, iOS, and embedded-device targets means more build variants, signing configurations, and device-specific test matrices — all of which reward automation.
• Shift to cloud-native architectures. Microservices and containerised workloads multiply the number of independently buildable components, making manual builds impractical.
• Developer productivity pressure. Engineering leaders track metrics like lead time for changes (from the DORA research programme). Fast, reliable builds are a direct input to that metric.
• Compliance and auditability requirements. Regulated industries (finance, healthcare) need a traceable, reproducible build trail. Automated pipelines with signed artifacts and immutable logs satisfy many of these requirements.

Common Challenges

Despite the clear benefits, teams routinely encounter friction when adopting or scaling build automation:

• Skills gap. Writing and maintaining complex pipeline-as-code (Jenkinsfiles, GitHub Actions workflows, Tekton pipelines) requires knowledge that many developers do not have on day one. Dedicated platform-engineering or DevOps teams are often needed.
• Flaky tests and flaky builds. Non-deterministic tests undermine confidence in automation. A build that randomly fails trains developers to ignore failures.
• Build performance at scale. Naive CI setups rebuild everything on every commit. As codebases grow, build times can balloon to 30–60+ minutes, eroding the feedback benefit. Solutions include incremental builds, remote caching (Bazel remote cache, Gradle Build Cache), and parallelisation.
• Secret and credential management. Pipelines need access to signing keys, cloud credentials, and API tokens. Poorly managed secrets are a significant security risk.
• Tool sprawl. Organisations often end up with multiple incompatible build systems (Maven for Java, npm scripts for front-end, shell scripts for infrastructure) that are hard to govern centrally.

Deployment and Licensing Models

The build automation market offers a range of deployment and commercial models, so teams can match tooling to their infrastructure and budget constraints:

• Cloud-based / SaaS – CircleCI, Bitrise, GitHub Actions, AWS CodeBuild. No infrastructure to manage; pricing typically based on compute minutes consumed.
• Self-hosted / on-premises – Jenkins, TeamCity, GitLab CE. Full control over the build environment, data residency, and network egress; requires operational overhead.
• Hybrid – CloudBees CI, GitLab EE. A SaaS control plane with self-hosted runners/agents for sensitive workloads.
• Open-source – Jenkins, Tekton, Drone, Woodpecker CI. Free to use; support and advanced features may require commercial add-ons.

Choosing a Build Automation Tool: Practical Criteria

When evaluating tools for your organisation, consider the following:

1. Language and framework support – Does the tool have first-class support for your primary tech stack (JVM, .NET, Node, Go, mobile)?
2. Pipeline-as-code – Can build definitions be versioned in source control alongside application code? This is now a baseline expectation.
3. Scalability – Can you add parallelism (multiple agents/runners) without re-architecting the pipeline?
4. Integration ecosystem – Does it connect to your source control, artifact repository, security scanners, and deployment tools?
5. Security posture – How are secrets managed? Can you restrict which pipelines can access which credentials?
6. Total cost of ownership – Factor in infrastructure, licensing, and engineering time to maintain the platform itself.
7. Vendor lock-in risk – How portable are your pipeline definitions if you need to migrate?

Summary

Build automation is no longer optional for teams that want to ship software reliably and frequently. The market includes a wide spectrum of tools — from open-source task runners and build systems to fully managed cloud CI/CD platforms — offered by vendors ranging from the Apache Software Foundation to AWS, Microsoft, and specialist players like Bitrise and Octopus Deploy. The right choice depends on your language ecosystem, team size, compliance requirements, and infrastructure preferences. Whatever tool you select, the fundamentals remain the same: version your pipeline definitions, keep builds fast and deterministic, manage secrets safely, and integrate build automation tightly with the rest of your delivery toolchain.

Original market research: Advance Market Analytics — Global Build Automation Software Market report (referenced April 2020 via sciencein.me). The underlying paid report was not independently reviewed for this article.

Google Cloud Buys Cornerstone Technology

Google Cloud acquired Cornerstone Technology, a firm specializing in mainframe-to-cloud migration services, with the explicit goal of helping enterprises running “Big Iron” workloads transition to private and public cloud infrastructure. The acquisition was announced in February 2020.

According to Howard Weale, Google’s Director of Transformation Practice, the core of Cornerstone’s value lies in its G4 platform — an automated toolchain that can decompose legacy mainframe programs written in COBOL, PL/1, or Assembler and recast them as cloud-native services running in managed, containerized environments.

“Through the use of automated processes, Cornerstone’s tools can break down your COBOL, PL/1, or Assembler programs into services and then make them cloud native, such as within a managed, containerized environment.”

Howard Weale, Director of Transformation Practice, Google Cloud

What the Cornerstone Platform Offers

Google stated that the Cornerstone technology would form the foundation of its future mainframe-to-Google Cloud product offerings. The platform is designed to address three common migration challenges:

• Migration roadmap assessment: Google will evaluate a customer’s existing mainframe environment and produce a structured roadmap toward a modern services architecture.
• Cross-language and cross-database conversion: The toolchain is designed to convert source code from one language to another (for example, COBOL to Java) and migrate data from legacy databases to modern equivalents.
• Automated workload migration: Automated pipelines move converted workloads onto Google Cloud infrastructure, reducing manual re-platforming effort.

Weale noted that a key use case is breaking apart mainframe monolithic applications into either Java monoliths or Java microservices — aligning legacy systems with contemporary service-oriented and microservices architectures that most enterprises now favor for new development.

Why This Matters for Google’s Enterprise Strategy

Mainframe workloads represent some of the most business-critical and deeply entrenched systems in industries such as banking, insurance, and government. Winning migration contracts in these sectors is a significant revenue opportunity — and a strong signal to large enterprises that a cloud provider can handle their most demanding requirements.

“Easy mainframe migration will go a long way as Google attracts large enterprises to its cloud.”

Matt Eastwood, Senior Vice President, Enterprise Infrastructure, Cloud, Developers and Alliances, IDC

The Cornerstone acquisition also positions Google Cloud more directly against competitors that already offer mainframe migration services, including Amazon Web Services, IBM/Red Hat, and Microsoft Azure. Each of these vendors has made investments in tooling or partnerships to help enterprises retire or re-platform mainframe workloads.

Analyst Caution: Mainframe Migration Is Not Always the Right Move

Despite the momentum behind cloud migration, Gartner issued a pointed warning — referenced in a report titled Considering Leaving Legacy IBM Platforms? Beware, as Cost Savings May Disappoint, While Risking Quality — advising organizations to scrutinize mainframe migration decisions carefully before committing.

“The value gained by moving applications from the traditional enterprise platform onto the next ‘bright, shiny thing’ rarely provides an improvement in the business process or the company’s bottom line. A great deal of analysis must be performed and each cost accounted for.”

Gartner, Considering Leaving Legacy IBM Platforms? Beware, as Cost Savings May Disappoint, While Risking Quality

Gartner’s core argument is that mainframes, while perceived as dated, continue to evolve. IBM and other vendors are actively integrating open-source tooling and modernizing hardware, making the platforms more accessible to developers. Application leaders were advised to re-evaluate the actual capabilities and total cost of ownership of their existing mainframe platforms before initiating a migration.

Key Takeaways for DevOps and Platform Engineering Teams

• Automated language conversion is the hard part. COBOL-to-Java conversion at scale has historically been error-prone and expensive. Evaluate any toolchain’s output quality rigorously before trusting automated conversions in production.
• Containerization is not free modernization. Wrapping a converted monolith in a container does not automatically make it cloud-native. Teams should plan for behavioral equivalence testing and performance validation post-migration.
• TCO analysis is essential. Mainframe licensing models differ fundamentally from cloud pricing. A detailed total-cost-of-ownership model, including staffing, testing, and ongoing cloud spend, is necessary before a business case can be made.
• Phased migration reduces risk. Rather than a full lift-and-shift, consider migrating lower-risk workloads first to validate tooling and build organizational confidence.
• Vendor lock-in trades. Moving from mainframe to a single cloud provider replaces one form of lock-in with another. Multi-cloud or abstraction-layer strategies should be considered where long-term portability matters.

Google Cloud’s acquisition of Cornerstone Technology signals serious intent to compete for enterprise mainframe migration budgets. However, as Gartner’s guidance underscores, the decision to migrate should be driven by rigorous analysis rather than vendor momentum — regardless of how compelling the automation tooling appears.

What Is the Global Big Data Market?

Big data is more than a description of large datasets. It refers to the full ecosystem of technologies used to capture, store, manage, and analyze large, fast-moving, and highly variable collections of data in order to solve complex business problems. Organizations adopt big data platforms to reduce the cost and complexity of managing data at scale, unlock analytical insights that traditional databases cannot deliver, and support real-time decision-making across the enterprise.

The term originally emerged to describe datasets too large for conventional database tools to handle. Today, it encompasses distributed storage systems, stream processing engines, data lakes, machine learning pipelines, and managed cloud analytics services.

Key Players in the Global Big Data Market

The global big data market is highly competitive, with established enterprise vendors, cloud hyperscalers, and specialist analytics companies all vying for market share. Major organizations active in this space include:

• IBM Corporation – enterprise data warehousing, AI-driven analytics (Watson), and hybrid cloud data platforms
• Oracle Corporation – cloud databases, Autonomous Data Warehouse, and integrated analytics
• Microsoft Corporation – Azure Synapse Analytics, HDInsight, and Power BI
• Amazon Web Services (AWS) – S3, EMR, Redshift, Kinesis, and a broad managed analytics portfolio
• Google – BigQuery, Dataflow, Dataproc, and Vertex AI
• SAP SE – HANA in-memory platform and enterprise analytics
• Hewlett Packard Enterprise (HPE) – Ezmeral Data Fabric and high-performance compute for analytics workloads
• Teradata Corporation – cloud-native data warehouse and Vantage analytics platform
• Splunk – machine data analytics, SIEM, and operational intelligence
• Cloudera – unified data platform combining legacy Hortonworks and Cloudera distributions on hybrid cloud
• Hortonworks – now merged with Cloudera; historically a major open-source Hadoop distribution vendor
• Palantir Technologies – data integration and analytics for government, defense, and enterprise
• Hitachi – industrial IoT data analytics and Lumada platform
• SAS Institute – statistical analytics, data management, and AI/ML tooling
• Dell Technologies – data storage infrastructure and converged analytics appliances
• Accenture – systems integration and managed analytics services
• Fujitsu – HPC-based analytics and enterprise IT services
• Datameer – data transformation and exploration on cloud data warehouses
• Actian – high-performance data integration and analytics database
• Guavus – telecom-focused big data analytics (acquired by Thales)

Data Types Covered by Big Data Solutions

Big data platforms are generally evaluated against three core data categories:

• Structured Data – Rows and columns stored in relational databases or data warehouses; amenable to SQL-based querying.
• Semi-Structured Data – Formats such as JSON, XML, and log files that carry some organizational tags but do not conform to a rigid schema.
• Unstructured Data – Free-form content including text documents, images, video, audio, and social media streams; typically requires specialized processing pipelines and AI/ML models to extract value.

Modern data lakehouse architectures aim to handle all three categories within a single platform, reducing the need for separate storage and processing silos.

Industry Verticals Driving Big Data Adoption

Demand for big data capabilities spans virtually every industry sector. The following verticals represent the primary application segments:

• Banking, Financial Services, and Insurance (BFSI) – fraud detection, risk modeling, algorithmic trading, and customer analytics
• Healthcare and Life Sciences – genomics, clinical trial analysis, patient data management, and predictive diagnostics
• Retail and Consumer Goods – personalization engines, supply chain optimization, and demand forecasting
• IT and Telecommunications – network performance monitoring, churn prediction, and service assurance
• Government and Defense – intelligence analysis, fraud detection in public programs, and smart city initiatives
• Manufacturing – predictive maintenance, quality control, and IoT sensor analytics
• Media and Entertainment – content recommendation, audience segmentation, and ad targeting
• Transportation and Logistics – route optimization, fleet management, and real-time tracking
• Energy and Utilities – smart grid analytics, predictive asset maintenance, and consumption forecasting
• Academia and Research – large-scale scientific datasets, climate modeling, and bioinformatics

Regional Market Landscape

Big data investment is global, though adoption rates and dominant use cases vary by region:

• North America (United States, Canada, Mexico) – the most mature market, driven by early cloud adoption, large technology vendors, and heavy investment in financial services and healthcare analytics
• Europe (UK, Germany, France, Italy, Spain, Russia) – growing adoption shaped by GDPR compliance requirements, which have heightened demand for data governance and privacy-preserving analytics
• Asia-Pacific (China, India, Japan, South Korea, Singapore, Indonesia, Australia) – the fastest-growing region, fueled by rapid digitalization, e-commerce expansion, and government-led smart city and manufacturing initiatives
• South and Central America (Brazil, Argentina, Chile) – emerging market with growth driven by fintech, agriculture analytics, and expanding cloud infrastructure
• Middle East and Africa (Saudi Arabia, UAE, South Africa, Turkey, Egypt) – investment focused on oil and gas analytics, smart government programs, and financial services modernization

What to Look for When Evaluating Big Data Market Research

DevOps and platform engineering teams often need to contextualize vendor decisions against broader market trends. When reviewing any big data market report, consider the following criteria:

• Forecast methodology – How are market size figures derived? Top-down (analyst estimates) and bottom-up (revenue aggregation) methods produce materially different numbers.
• Coverage scope – Does the report include open-source platforms (Apache Spark, Flink, Kafka) alongside commercial vendors? Open-source adoption can be underrepresented in revenue-based market sizing.
• Segmentation granularity – Country-level or sub-vertical breakdowns are typically locked behind premium report tiers.
• Recency – The big data vendor landscape shifts quickly. Cloudera and Hortonworks merged in 2019; Guavus was acquired. Reports older than 12–18 months may reflect an outdated competitive picture.
• Data type treatment – Ensure the report distinguishes between big data infrastructure (storage, compute) and analytics software layers, as growth rates differ substantially.

Conclusion

The global big data market encompasses a wide range of vendors, data types, and industry use cases. Established enterprise software companies, cloud hyperscalers, and specialist analytics vendors continue to compete and consolidate, driven by enterprise demand for scalable, cost-effective data platforms. For DevOps and data engineering practitioners, understanding the competitive landscape helps inform technology selection, vendor negotiation, and long-term platform strategy. Validating any specific revenue figures or growth forecasts against primary sources or multiple independent research firms before using them in business cases is strongly recommended.

Market Overview

The global application container market was valued at approximately USD 1.1 billion in 2017 and was projected to grow at a compound annual growth rate (CAGR) of 33.2% between 2018 and 2026, according to research published by Data Bridge Market Research. The rapid adoption of microservices architectures, the push for DevOps practices, and enterprise demand for portability and scalability have all contributed to this growth trajectory.

What Is an Application Container?

An application container is a lightweight, standalone executable package that bundles an application’s code together with its dependencies, runtime, libraries, and configuration files. Containers solve one of the most persistent problems in software delivery: ensuring that an application runs consistently regardless of where it is deployed — whether on a developer’s laptop, a test server, an on-premises data center, or a public cloud instance.

Unlike traditional hypervisor-based virtualization — where each virtual machine runs its own full operating system — container-based virtualization allows multiple containers to share a single host OS kernel. Each container is isolated at the process level and carries only its own application code and dependencies. This approach results in:

• Faster startup times compared to full virtual machines
• Lower resource overhead due to shared OS kernel usage
• Greater density — more workloads per physical host
• Consistent environments across development, staging, and production

Cloud-based deployment has emerged as the dominant adoption pattern for container technology, enabling elastic scaling and simplified orchestration across distributed infrastructure.

Market Drivers and Restraints

Key Growth Drivers

• Microservices adoption: Organizations decomposing monolithic applications into microservices naturally gravitate toward containers as the standard packaging and deployment unit.
• Modernization of legacy applications: Enterprises are containerizing existing business-critical workloads to reduce infrastructure costs and improve deployment velocity.
• Business agility: Containers shorten the feedback loop between development and production, enabling faster iteration and competitive differentiation.
• Cloud-native development trends: Major cloud providers offering managed container services has lowered the barrier to entry significantly.

Market Restraints

• Security concerns: Shared kernel architecture introduces risks such as container escape vulnerabilities and insufficient isolation for certain compliance-sensitive workloads.
• Skills gap: Expertise in container orchestration, networking, and security remains scarce, increasing implementation complexity and time-to-value.

Market Segmentation

By Platform

The platform segment covers the orchestration and runtime technologies used to deploy and manage containers at scale:

• Kubernetes — The dominant open-source container orchestration platform, originally developed by Google and now maintained by the CNCF.
• Docker Swarm — Docker’s native clustering and orchestration solution, simpler to configure than Kubernetes but with a narrower feature set.
• AWS ECS (Elastic Container Service) — Amazon’s proprietary managed container orchestration service, tightly integrated with the AWS ecosystem.
• Apache Mesos — A distributed systems kernel capable of running containers alongside other workloads.
• HashiCorp Nomad — A flexible workload orchestrator that supports containers, VMs, and standalone binaries.
• Cloud Foundry — An open-source PaaS platform with built-in container runtime support.
• OpenStack Magnum — An OpenStack API service that provisions container orchestration engines.
• Azure Container Service / AKS — Microsoft Azure’s managed Kubernetes offering.
• Internally Developed Tools — Custom-built orchestration solutions used by large enterprises with specific requirements.

By Service

• Consulting
• Container monitoring
• Container security
• Container data management
• Container networking
• Container orchestration
• Support and maintenance

By Deployment Model

• Cloud — Public, private, and hybrid cloud deployments
• On-premises — Self-managed data center deployments

By Organization Size

• Large enterprises
• Small and medium-sized enterprises (SMEs)

By Application Area

• Production workloads
• Collaboration tools
• Application modernization
• Resource optimization and content management

By Industry Vertical

• Banking, Financial Services, and Insurance (BFSI)
• Healthcare and life sciences
• Telecom and IT
• Government and defense
• Energy and utilities
• Manufacturing
• Retail
• Education, media, and entertainment

By Geography

The report covers 28 countries across five major regions:

• North America — United States, Canada
• Europe — Germany, France, United Kingdom, Netherlands, Switzerland, Turkey, Russia
• Asia-Pacific — China, India, Japan, South Korea, Australia, Singapore
• South America — Brazil and others
• Middle East & Africa — Saudi Arabia, South Africa and others

Competitive Landscape

The global application container market is highly fragmented. Vendors have pursued growth through a combination of product launches, strategic acquisitions, partnerships, and geographic expansion. Key players identified in the research include:

• Hyperscalers & Cloud Platforms: Amazon Web Services (AWS), Microsoft Azure, IBM
• Open-Source Ecosystem Leaders: Red Hat (now part of IBM), CoreOS (acquired by Red Hat), VMware
• Orchestration & Infrastructure: Mesosphere, Rancher Labs (acquired by SUSE), HashiCorp
• Container Security: Twistlock (acquired by Palo Alto Networks), Draios (Sysdig)
• Storage & Networking: Portworx (acquired by Pure Storage), Weaveworks, Nimble Storage
• Platform Services: Cisco, BlueData, Apprenda, Apcera

Since the original report was published in 2020, significant consolidation has occurred in this space. Several of the vendors listed above have been acquired by larger platform vendors, reflecting the maturing nature of the market.

Related Video: Container Orchestration Platforms

Research Methodology

According to Data Bridge Market Research, the report used a data triangulation methodology combining:

• Primary research — Interviews with industry experts and practitioners
• Secondary research — Analysis of published data, company filings, and industry sources
• Quantitative modeling — Statistical and market coherence models to derive forecasts

Analytical frameworks applied include vendor positioning grids, market timeline analysis, company market share analysis, and top-down/bottom-up sizing approaches.

Key Takeaways for DevOps Teams

While specific market figures require independent verification, the structural trends identified in this research remain directionally relevant for engineering and platform teams:

• Kubernetes has established itself as the de facto standard for container orchestration across cloud and on-premises environments.
• Managed container services from hyperscalers (AWS ECS/EKS, Azure AKS, Google GKE) continue to absorb significant market share from self-managed alternatives.
• Container security and observability tooling has become a standalone market category, not merely a feature of orchestration platforms.
• SME adoption of containers has grown substantially as managed offerings reduce operational complexity.
• Multi-cloud and hybrid deployment strategies are driving demand for platform-agnostic orchestration tooling.

Source: Market data originally attributed to Data Bridge Market Research via scienceofchange.org (published 2020). Figures should be treated as historical estimates, not current market data.

What Are Software Containers?

A software container packages an application and all its dependencies — libraries, configuration files, and runtime — into a single, portable unit. Unlike virtual machines, containers share the host operating system kernel, making them significantly lighter in terms of resource consumption and startup time.

Key operational characteristics of containers include:

• Portability: A container image runs consistently across development laptops, on-premises servers, and public cloud platforms without modification.
• Isolation: Each container runs in its own isolated process space, reducing the risk of dependency conflicts between applications.
• Efficiency: Containers start in seconds and consume far less memory and CPU overhead than equivalent virtual machines.
• Scalability: Orchestration platforms such as Kubernetes allow containers to be scaled horizontally in response to demand.
• Security: Namespace and cgroup-based isolation limits the blast radius of a compromised process, though containers require careful configuration to harden effectively.

Market Segments

The software containers market is typically analyzed along two dimensions: the platform or device type where containers are deployed, and the application use case they serve.

By Deployment Platform

• Server and data center environments (the dominant segment)
• Desktop and developer workstations
• Edge and mobile devices (an emerging area as container runtimes are optimized for constrained hardware)

By Application Use Case

• Monitoring and Logging: Running observability tooling (e.g., Prometheus exporters, log shippers) as sidecar containers alongside application workloads.
• Security: Scanning container images for vulnerabilities, enforcing runtime policies, and isolating sensitive workloads.
• Continuous Deployment: Containers are a natural fit for CI/CD pipelines — each build produces an immutable image that progresses through test, staging, and production environments unchanged.
• Management and Orchestration: Platforms such as Kubernetes, Docker Swarm, and Amazon ECS automate scheduling, health checking, and rolling updates across container fleets.
• Networking and Data Management: Container networking interfaces (CNI plugins) and persistent volume drivers extend containers into complex multi-service architectures and stateful workloads.

Key Vendors in the Software Containers Ecosystem

The 2019 market analysis identified the following major players as central to the software containers landscape. Their roles and primary offerings are summarized below:

• Docker: Popularized the container image format and the Docker Engine runtime. Docker Hub remains one of the largest public container registries.
• Google: A major contributor to Kubernetes (originally an internal Google project) and operator of Google Kubernetes Engine (GKE).
• Amazon Web Services: Offers Amazon Elastic Container Service (ECS), Amazon Elastic Kubernetes Service (EKS), and Amazon Elastic Container Registry (ECR).
• Microsoft Corporation: Provides Azure Kubernetes Service (AKS) and Windows container support, and is a significant contributor to open-source container tooling.
• IBM Corporation: Offers IBM Cloud Kubernetes Service and has integrated container capabilities across its hybrid cloud and OpenShift-based offerings.
• VMware: Focused on integrating containers with existing virtualization infrastructure, notably through Tanzu and vSphere with Kubernetes.
• CoreOS (acquired by Red Hat, itself acquired by IBM): Contributed foundational container technologies including etcd, rkt, and the Container Linux operating system.
• Codenvy (acquired by Red Hat): Provided cloud-based developer workspaces built on container technology, later integrated into Eclipse Che.

Regional Adoption Landscape

The 2019 report covered container adoption across seven major regions. Adoption patterns reflect the maturity of local cloud infrastructure, enterprise digital transformation initiatives, and the availability of skilled DevOps talent:

• North America: The largest and most mature market, driven by hyperscaler investment and high enterprise cloud adoption rates.
• Europe: Significant adoption, with GDPR and data sovereignty requirements influencing hybrid and private cloud container deployments.
• China: Rapid growth driven by major domestic cloud providers (Alibaba Cloud, Tencent Cloud, Huawei Cloud) offering managed Kubernetes services.
• Japan: Steady enterprise adoption, particularly in financial services and manufacturing sectors undergoing IT modernization.
• Southeast Asia: Fast-growing adoption correlated with expanding cloud infrastructure investment across the region.
• India: A large pool of container-skilled engineers combined with a growing startup ecosystem contributing to both supply and demand.
• Central and South America: An emerging market with adoption concentrated in Brazil and Mexico.

Growth Drivers and Market Restraints

Drivers

• Broad enterprise shift to microservices architectures, which map naturally to container-per-service deployment patterns.
• Cloud-native development practices promoted by the Cloud Native Computing Foundation (CNCF) and major cloud vendors.
• Demand for faster software delivery cycles through CI/CD automation, where immutable container images reduce environment drift.
• Cost optimization pressure encouraging organizations to consolidate workloads more efficiently than traditional VM-based approaches allow.

Restraints

• Operational complexity of running Kubernetes at scale, including networking, storage, and security configuration challenges.
• Security concerns around container image provenance, misconfigured registries, and shared kernel vulnerabilities.
• Skills gap: demand for experienced Kubernetes and container platform engineers consistently outpaces supply.
• Legacy application compatibility — not all applications benefit from or are suited to containerization without significant refactoring.

Strategic Takeaways for DevOps Teams

Regardless of market forecast figures, several strategic realities have proven durable since 2019:

• Container adoption is not a binary decision. Organizations typically run hybrid environments with containers, VMs, and bare metal co-existing based on workload requirements.
• Orchestration maturity matters more than runtime choice. Kubernetes has effectively become the standard orchestration layer; investing in Kubernetes expertise delivers long-term value regardless of the underlying cloud provider.
• Security must be built in from the image build stage — not bolted on at runtime. Supply chain security (image signing, SBOM generation, vulnerability scanning in CI) has become a critical practice.
• Managed Kubernetes services (EKS, GKE, AKS) significantly lower the operational burden compared to self-managed clusters for most organizations.

This article is based on a 2019 market analysis report originally summarized at spacemarketnow.com. Specific market size figures, revenue forecasts, and CAGR percentages from the original report have been omitted as they could not be independently verified. Readers requiring precise forecast data should consult the primary report directly.

Red Hat’s Strategy: One Platform for CNFs and VNFs

Red Hat has been working to make OpenShift a first-class platform for telecommunications network functions, aiming to support both containerized network functions (CNFs) and virtualized network functions (VNFs) on Kubernetes. As of late 2019, these workloads ran on separate Red Hat products: VNFs on Red Hat OpenStack, and containerized applications on OpenShift. Red Hat’s goal was to converge these platforms.

“Today these are two separate products from Red Hat,” Azhar Sayeed, Red Hat Chief Architect for Telecommunications, told Light Reading. “From our perspective, it’s about preparing the infrastructure to run VNFs and CNFs.”

OpenShift was already a strategically important product for Red Hat, extending Red Hat Enterprise Linux (RHEL) into cloud-native infrastructure. It runs on-premises, on bare metal, on private clouds (OpenStack, VMware), and on public clouds including AWS, Microsoft Azure, and Google Cloud.

Key OpenShift Enhancements for Telecommunications

Red Hat was expanding OpenShift capabilities specifically to address the unique requirements of network functions. The enhancements covered several critical areas:

• Performance and hardware acceleration: Necessary for latency-sensitive network workloads that previously relied on purpose-built hardware.
• IPv6 support: Upstream Kubernetes was expected to receive IPv6 support, a requirement for many telco network functions. Red Hat indicated it expected to support IPv6 within approximately three months of the October 2019 announcement.
• IP networking enhancements: Improvements to support the demanding IP networking requirements inherent in CNFs.
• Application lifecycle management: Extended capabilities for upgrading, deploying, packaging, and managing network functions — covering compute, network, and storage resources. Red Hat was bringing to applications the same lifecycle management it already provided for infrastructure.

Earlier in May 2019, Red Hat had also added capabilities relevant to telco operators: service mesh for virtual networking between microservices, Knative for serverless application support, and a partnership with Microsoft on Kubernetes-based Event-Driven Autoscaling (KEDA).

Why CNFs Matter: The Case Over VNFs

Network Function Virtualization (NFV) emerged as a way for telecommunications operators to reduce vendor lock-in by replacing proprietary hardware appliances with software running on commercial off-the-shelf (COTS) servers. The first generation of NFV produced VNFs — network functions running inside virtual machines (VMs).

CNFs represent the next evolution. Sayeed explained the key architectural difference: each VM carries its own full copy of the operating system, database, and supporting infrastructure, while containers share those layers. This makes CNFs significantly lighter weight. The practical advantages include:

• Higher workload density: More network functions can run on the same hardware.
• Improved resource efficiency: Shared OS and runtime layers reduce overhead.
• Better uptime characteristics: Microservices-based design supports independent scaling and faster recovery.
• Potential performance benefits: Reduced overhead can translate to lower latency in suitable configurations.

Beyond the technical advantages of containers themselves, running CNFs on Kubernetes creates an operational benefit: operators can use a single, unified platform for both IT workloads and network functions. This consolidation can improve utilization of hardware, reduce software licensing complexity, and lower the skills burden on engineering teams.

The Real Bottleneck: CNF Availability from Vendors

Sayeed was candid about where the real constraint lay. The major bottleneck to deploying CNFs on OpenShift was not gaps in OpenShift’s capabilities — it was the limited availability of CNFs from network equipment vendors. As of late 2019, the primary CNFs available were load balancers, firewalls, and policy control functions. Vendors that had committed to supporting OpenShift with network functions included Metaswitch and Clearwater.

This vendor readiness gap is a common pattern in platform transitions: the infrastructure platform can mature faster than the application ecosystem that must run on it.

Operator Adoption and IBM Acquisition Context

OpenShift had attracted meaningful support from telecommunications service providers. AT&T included OpenShift as part of a strategic alliance with IBM, Red Hat’s parent company following IBM’s $34 billion acquisition of Red Hat, which closed in July 2019. Turkcell, operating with more than 35 million subscribers and near-total population coverage in Turkey, was relying on Red Hat OpenStack to support an ambitious target of virtualizing 40% of its network by the end of 2019.

The IBM acquisition provided Red Hat with additional resources and enterprise reach, while keeping Red Hat’s open-source model intact — a critical requirement for maintaining operator and developer trust in OpenShift as a neutral platform.

KubeCon 2019 Demonstration

Red Hat planned to demonstrate an end-to-end 5G network running on OpenShift at KubeCon North America 2019 in San Diego. The demonstration was intended to show the platform running CNFs both on-premises and in the public cloud, providing a practical proof point for the telco-on-Kubernetes story Red Hat was building.

Takeaways for DevOps and Platform Engineers

• Kubernetes is increasingly being positioned not just for IT workloads, but for telecommunications network control plane functions — a significant expansion of its operational scope.
• The convergence of VNF (OpenStack-based) and CNF (Kubernetes-based) infrastructure is an active engineering challenge, not a solved problem.
• Features like IPv6 support, hardware acceleration, and robust application lifecycle management are prerequisites for running production network functions on Kubernetes — not nice-to-haves.
• The ecosystem readiness of CNF vendors is a practical constraint that platform maturity alone cannot solve.
• Operators evaluating Kubernetes for network functions should assess both platform capabilities and the CNF vendor ecosystem for their specific function types.

“Companies from all sectors of the German economy are using the AWS cloud to promote innovation, substantially cut costs, securely use their data, and at the same time support their growth in Germany, Europe, and worldwide,” said Klaus Bürg, General Manager for Amazon Web Services in the DACH region, at the opening ceremony. “The new AWS office in Munich with the Digital Builders Showroom, the Industrial Software Showcase, and the Event Area is another part of our ongoing investment to further promote cloud initiatives in Germany.”

What the Munich Office Offers

The Munich office comprises three distinct areas, each targeting a different aspect of cloud adoption: a hands-on innovation showroom, an industrial manufacturing showcase, and a community event space. Together they are intended to make cloud and emerging technologies more accessible and tangible for German organisations.

Digital Builders Showroom

Developed in partnership with Boston Consulting Group (BCG) and Siemens, the Digital Builders Showroom gives organisations of any size the opportunity to learn about and experiment with AWS cloud technologies. The showroom is structured around a series of interactive stations where visitors can see real AWS-based applications in operation, including Smart Factory and Smart City solutions.

Consultants with both technical and management backgrounds are on hand to demonstrate how technologies including Artificial Intelligence (AI), Deep Learning, Computer Vision, Edge Computing, and Natural Language Processing (NLP) can drive business growth and innovation.

“In collaboration with AWS, we will bring digital transformation to life for German companies of all sizes in the Digital Builders Showroom,” said Matthias Tauber, CEO of Boston Consulting Group Germany. “We want to make digital transformation more tangible. To achieve this, our consultants will work closely with the experts at AWS. We will help organizations of all sizes better understand disruptive technologies such as AI, Computer Vision, Edge Computing, and Internet of Things (IoT), and help promote innovation in all German companies.”

Industrial Software Showcase

Built in partnership with Siemens Digital Industries Software, the Industrial Software Showcase focuses specifically on manufacturing. It demonstrates how cloud technologies can improve every step of the manufacturing lifecycle — from initial design through production to ongoing inspection and maintenance. Visitors can gain hands-on experience by testing advanced manufacturing applications themselves.

“Manufacturing companies are undergoing rapid change. New analytical methods, machine learning, IoT, and other technologies that are changing basic operating procedures are all contributing to this change,” said Tony Hemmelgarn, President and CEO of Siemens Digital Industries Software. “AWS is a pioneer and market leader in cloud computing. Working closely together, we want to help industrial companies make the cultural and technological changes necessary to succeed in these disruptive times.”

Event Space and Community Hub

Beyond the showrooms, the Munich office includes meeting rooms, a large auditorium, and co-working areas where customers and AWS Partner Network (APN) partners can collaborate with AWS business and technical specialists. Regular training and certification courses are also held in dedicated public classrooms, giving developers and technical professionals structured pathways to expand their cloud knowledge.

The AWS Loft programme — a series of events designed to help startups build and scale on AWS — will also run out of this space. The first AWS Loft at the Munich office was scheduled to open for two weeks starting 4 November 2019.

AWS Hiring and Expansion Across Germany

The Munich office opening is part of a broader AWS expansion across Germany. At the time of the announcement, AWS stated plans to hire more than 500 people during 2019 across its data centres, development centres, and offices in Berlin, Cologne, Dresden, Frankfurt, Hamburg, Munich, and Walldorf. Amazon also announced plans to create a total of 2,800 new jobs in Germany by the end of 2019.

Original reporting sourced from innovationorigins.com. Opening ceremony quotes and figures are attributed to statements made at the event.

Overview of the Global Public Cloud Service Market

The global public cloud service market entered 2019 as one of the fastest-growing segments of the technology industry. Enterprises across virtually every vertical were accelerating their migration from on-premises infrastructure to cloud-based platforms, driven by cost flexibility, scalability, and the broadening maturity of cloud-native tooling.

A market research report titled Public Cloud Service Market Research Report 2018–2026, published by Innovate Insights, examined the competitive dynamics, regional distribution, and service-type segmentation of this market. The following sections summarise the report’s key structural findings for practitioners who want a concise reference without marketing filler.

Key Players in the Public Cloud Service Market

The report identified the following companies as the primary competitors in the global public cloud service landscape:

• Amazon Web Services (AWS) – the longest-established hyperscale cloud provider, dominant in IaaS and PaaS revenue share.
• Microsoft Azure – strong enterprise penetration, particularly in hybrid cloud and Windows-integrated workloads.
• Google Cloud Platform (GCP) – competitive in data analytics, machine learning, and Kubernetes-native workloads.
• IBM Cloud – focused on regulated industries and hybrid deployments, reinforced by the Red Hat acquisition.
• Oracle Cloud – targeting database-heavy workloads and ERP migrations.
• Salesforce – dominant in SaaS CRM; a significant force in the broader public cloud ecosystem.
• SAP – enterprise application SaaS and platform services, particularly in ERP and supply chain.
• VMware – hybrid cloud infrastructure and virtualisation-layer services.
• Rackspace – managed cloud services across multiple provider platforms.
• Red Hat – open-source PaaS and container platform services (OpenShift).
• Adobe – creative and marketing SaaS on its own cloud infrastructure.
• Verizon Cloud – telecom-adjacent cloud and managed hosting services.
• Navisite – managed cloud for mid-market enterprises.
• Dropbox – cloud storage and collaboration SaaS.
• Egnyte – enterprise content collaboration and governance.

The report examined each player’s market share, production and service delivery locations, pricing models, gross margins, and product portfolio breadth.

Market Segmentation by Service Type

The public cloud service market is broadly segmented by the type of service delivered. The report categorised offerings into the following segments:

Infrastructure as a Service (IaaS)

IaaS provides virtualised compute, storage, and networking resources on demand. AWS EC2, Azure Virtual Machines, and Google Compute Engine are canonical examples. Organisations consume IaaS to replace or augment physical data centres without long-term capital commitments.

Platform as a Service (PaaS)

PaaS layers a managed runtime, middleware, and developer toolchain on top of IaaS. It removes the need for teams to patch or scale the underlying OS and runtime. Examples include AWS Elastic Beanstalk, Azure App Service, and Google App Engine.

Software as a Service (SaaS)

SaaS delivers fully managed applications over the internet under a subscription model. Salesforce, Office 365, Google Workspace, Dropbox, and Adobe Creative Cloud are among the largest SaaS products by revenue.

Cloud Bursting

Cloud bursting is a hybrid architecture pattern in which a workload running on private infrastructure automatically expands into a public cloud when local capacity is exhausted. It is particularly relevant for batch processing and seasonal traffic spikes.

Multi-Tenant Computing

Multi-tenancy is a core architectural principle of public cloud, allowing a single provider infrastructure to serve many customers with logical isolation between them. It is foundational to the economies of scale that make public cloud pricing competitive.

Resource Pooling

Resource pooling refers to the provider’s ability to aggregate physical resources and dynamically allocate them across customers, a core characteristic in NIST’s definition of cloud computing.

Market Segmentation by Vertical / Application

Demand for public cloud services was analysed across the following industry verticals in the report:

• Government – sovereign cloud requirements, compliance mandates, and digital transformation of citizen services.
• Private Organisations – broad enterprise adoption across manufacturing, retail, media, and professional services.
• Academics and Education – remote learning platforms, research computing, and student information systems.
• Banking, Financial Services, and Insurance (BFSI) – high-compliance workloads, real-time payments infrastructure, and risk analytics.
• Healthcare – electronic health records, medical imaging, and AI-assisted diagnostics, subject to HIPAA and regional equivalents.
• Supply Chain Management – real-time inventory tracking, demand forecasting, and logistics optimisation.

Geographical Scope

The report’s regional analysis covered the following geographies, with attention to market share, projected growth rates, and emerging opportunities in each:

• United States – the largest single market, home to the three dominant hyperscalers.
• China – a distinct competitive environment dominated by Alibaba Cloud, Tencent Cloud, and Huawei Cloud, with significant regulatory separation from Western providers.
• Europe – growth driven by GDPR compliance requirements and increasing data residency demands, with rising sovereign cloud initiatives.
• Japan – mature enterprise IT market undergoing cloud-first policy adoption.
• Southeast Asia – high-growth emerging market with rapid mobile-first cloud adoption.
• India – rapidly expanding developer ecosystem, significant public-sector cloud initiatives, and growing startup demand.

Key Market Dynamics

Drivers

• Shift from capital expenditure to operational expenditure models.
• Growing demand for elastic compute for AI, ML, and big data workloads.
• Proliferation of DevOps and CI/CD practices requiring on-demand provisioning.
• Remote work and distributed team enablement.

Challenges

• Data sovereignty and cross-border data transfer restrictions.
• Vendor lock-in risk with proprietary managed services.
• Security and shared-responsibility model complexity.
• Unpredictable egress and inter-region data transfer costs.

Opportunities

• Edge computing extending cloud capabilities closer to end users and IoT devices.
• Multi-cloud strategies reducing dependence on any single provider.
• Expansion of cloud marketplace ecosystems enabling ISV distribution.
• Managed Kubernetes and serverless platforms lowering the barrier to cloud-native development.

Report Structure at a Glance

For practitioners assessing whether the full Innovate Insights report suits their needs, the table of contents covers the following major areas:

• Global market size (volume and value) and forecast through 2026.
• Regional market share breakdown and growth rate projections.
• Competitive landscape with company profiles, pricing models, and gross margins.
• Service-type segmentation with revenue and growth data per segment.
• Vertical application segmentation with demand and growth data.
• Marketing channel analysis (direct vs. indirect).
• Market dynamics: drivers, challenges, and influence factors.
• Research methodology and data triangulation approach.

Takeaways for DevOps and Cloud Engineering Teams

Market reports of this type are most useful when cross-referenced with your own procurement and architecture decisions. A few practical considerations:

• Provider selection – AWS, Azure, and GCP continue to dominate IaaS/PaaS revenue. Niche providers (Rackspace, Navisite) remain competitive for managed service overlays rather than raw infrastructure.
• Service model clarity – Clearly distinguishing IaaS, PaaS, and SaaS responsibilities is essential for security posture and cost attribution. Each model shifts a different portion of the shared-responsibility stack to the provider.
• Vertical compliance – BFSI and healthcare workloads require explicit verification of provider certifications (SOC 2, ISO 27001, HIPAA BAA, PCI DSS) before migration planning.
• Multi-cloud strategy – The breadth of named competitors in this space underscores why multi-cloud governance tooling (cost management, policy enforcement, identity federation) has become a primary concern for platform engineering teams.

Source: themarketsegment.com / Innovate Insights – Public Cloud Service Market Research Report 2018–2026 (referenced September 2019).

What the AWS Migration Competency Means

Datadog, the monitoring and analytics platform for cloud-native environments, announced in July 2019 that it had achieved Amazon Web Services (AWS) Migration Competency status. The designation is awarded through the AWS Partner Network (APN) Competency Program and signals that a technology partner has demonstrated deep AWS expertise, proven customer success, and the ability to support complex migration projects end-to-end.

For Datadog specifically, the recognition is scoped to Technology for Application Profiling — reflecting its strength in giving engineering teams the observability they need to assess, move, and validate applications on AWS.

The AWS Competency Program

AWS established the Competency Program to help customers cut through the noise when evaluating APN partners. Earning a competency badge requires partners to:

• Pass a rigorous technical validation of their solution by AWS.
• Demonstrate proven, verifiable customer success stories in the competency area.
• Maintain active APN membership in good standing.

The Migration Competency specifically covers the full migration lifecycle: discovery, planning, migration, and ongoing operations. Partners that hold this status have shown they can support customers at every stage, not just the initial lift-and-shift.

Why Observability Matters During Cloud Migration

Cloud migrations routinely surface unexpected performance problems, configuration gaps, and dependency issues that are invisible until workloads move. Without deep telemetry, teams often discover these problems only after user-facing impact has already occurred.

Datadog’s platform addresses this by correlating several data streams in a single pane of glass:

• Infrastructure metrics — host-level and container-level performance data from both on-premises sources and AWS services.
• Application performance monitoring (APM) / distributed traces — end-to-end request traces that reveal where latency or errors are introduced.
• Log management — centralized, searchable log data correlated with metrics and traces.
• Synthetic monitoring — scripted tests that continuously validate user-facing endpoints from external vantage points.

During a migration, teams can use this data to baseline on-premises performance, compare it against AWS behavior in real time, and catch regressions before cutover.

“Our customers migrating to AWS need visibility throughout the process to ensure a smooth transition, and we look forward to supporting them with our comprehensive cloud monitoring platform.”

— Ilan Rabinovitch, VP of Community and Product, Datadog (2019)

Datadog’s AWS Integration Breadth

At the time of the announcement, Datadog supported a wide range of AWS services through its integration library, enabling teams to pull CloudWatch metrics, VPC flow logs, and service-level data from managed offerings such as RDS, ECS, EKS, Lambda, S3, and more — without requiring custom instrumentation for each service.

This breadth is particularly valuable during migrations because it allows teams to monitor both legacy infrastructure and new AWS resources inside the same dashboards and alerting rules, reducing operational blind spots during the transition window.

About Datadog

Datadog is a SaaS-based monitoring and analytics platform designed for developers, IT operations teams, and business users. Its core product integrates infrastructure monitoring, APM, and log management into a unified observability stack. Organizations across industries use Datadog to accelerate cloud adoption, reduce mean time to resolution (MTTR), and maintain service reliability at scale.

For more information on Datadog’s AWS integrations and migration support, visit datadoghq.com.

The Problem: AWS Session Cookies That Outlive Logout

Phishing remains one of the most reliable attack vectors in both offensive security and real-world attacks. It is effective against cloud platforms just as it is against on-premises services — and a discovery by Spencer Gietzen at Rhino Security Labs highlights a particularly persistent threat specific to Amazon Web Services.

Gietzen found that authentication cookies issued by the AWS web console remain valid for approximately 12 hours — even if the account owner changes their password and explicitly logs out after suspecting a compromise. This means an attacker who successfully phishes a victim’s AWS credentials and captures the session cookie has a significant window of persistent access, independent of any subsequent remediation steps the victim takes.

How the Attack Works: Reverse-Proxy Phishing

Standard phishing captures a username and password but is often defeated by multi-factor authentication (MFA). A more sophisticated variant uses a reverse proxy positioned between the victim and the legitimate service. In this setup:

1. The victim is directed to a phishing page that transparently proxies the real AWS login page.
2. Because the content is proxied from the real AWS site, the page looks and behaves identically to the genuine one.
3. When the victim enters credentials and an MFA code, the proxy intercepts both, uses them to create a valid session with AWS, and then redirects the victim to the real AWS site.
4. The victim’s credentials, MFA code, and — critically — the resulting authentication cookie are all captured and stored.

Gietzen used Modlishka, an open-source reverse-proxy phishing framework, to demonstrate this technique during a customer penetration testing engagement.

“Because Modlishka is just proxying the AWS web page to our target user, it will be able to phish them, regardless of whether or not they have MFA enabled. Once the user is prompted for their MFA code and enters it, Modlishka will interject. It will create a valid login session with the AWS web console, store the details, then send the target user off to the actual AWS website and away from our phishing page. The user’s credentials and cookies will be stored in the cookie jar.”

— Spencer Gietzen, Rhino Security Labs

The Cookie Persistence Issue

In most services, session cookies are invalidated when a user logs out or changes their password. AWS behaves differently. Gietzen’s testing showed:

• Stolen AWS session cookies remained valid for approximately 12 hours after issuance.
• Changing the account password did not invalidate the stolen cookie.
• Logging out of the web console did not invalidate the stolen cookie.
• Removing and replacing the MFA device on the account did not invalidate the stolen cookie.
• Changing the password and logging out simultaneously still did not invalidate the stolen cookie.

“We found that after doing this, the session was still valid. This showed us that even after a target user changes their IAM user’s password and logs out, the phished cookies could still create a multi-factor authenticated session to the AWS web console.”

— Spencer Gietzen, Rhino Security Labs

Amazon’s Response: Intended Behavior

When Gietzen reported his findings to Amazon, the company confirmed that the long-lived cookie behavior is intentional. The design is meant to support workloads and sessions that run for extended periods, even after the initiating user logs out of the console.

Because AWS has classified this as expected behavior rather than a vulnerability, it is unlikely to be patched. This places the burden of defense entirely on AWS users and administrators.

Scope and Limitations of the Attack

This technique targets the AWS Identity and Access Management (IAM) system and the AWS web console specifically. Several important constraints apply:

• The attack is effective against software-based MFA (such as authenticator apps) and hardware OTP devices.
• The attack does not work against hardware-based U2F tokens (such as a YubiKey), because U2F performs origin binding — the key’s cryptographic challenge response is tied to the legitimate domain URL and cannot be replayed through a proxy.
• However, U2F tokens are not universally supported across all AWS workflows, including CLI-based access, which limits their applicability as a complete solution.

Defensive Recommendations

Gietzen outlined several mitigations, ranging from highly effective to partially effective:

1. Use a Hardware U2F Security Key

This is the strongest defense. U2F keys such as a YubiKey perform origin verification in the browser, meaning they will refuse to respond to authentication challenges from a phishing domain, even one that perfectly mirrors the legitimate AWS login page. This blocks the reverse-proxy attack entirely at the MFA step.

2. Eliminate IAM Users in Favor of IAM Roles and Temporary Credentials

IAM users have long-lived credentials tied to usernames and passwords — exactly the combination that makes this attack possible. Migrating to IAM roles with temporary, automatically-rotating credentials (via AWS STS) significantly reduces the attack surface. Temporary credentials have short lifespans and cannot be phished in the same way as persistent login sessions.

3. Enforce IP-Based or Context-Based Access Policies

AWS IAM policies can be scoped to restrict console access to known IP ranges or specific conditions. This does not prevent cookie theft but may limit where stolen cookies can be used.

4. Monitor CloudTrail for Unusual Console Activity

AWS CloudTrail logs API and console activity. Unusual login times, geographic anomalies, or activity shortly after a password change should be treated as indicators of compromise and investigated promptly.

Key Takeaways for AWS Administrators

• MFA alone does not protect against reverse-proxy phishing — the MFA code is intercepted in real time and used immediately.
• AWS console session cookies survive password changes and logouts for up to approximately 12 hours by design.
• Hardware U2F keys are the most effective phishing-resistant MFA option currently available for the AWS console.
• Reducing reliance on IAM users with long-lived credentials is a sound architectural defense — prefer roles and temporary credentials wherever possible.
• Incident response plans should account for the possibility that session cookies are still valid even after credentials have been rotated.

The research underscores a broader principle: credential rotation alone is insufficient incident response when session tokens are independent of the underlying credentials. AWS users should treat any suspected phishing event as requiring both credential rotation and session revocation — and should be aware that, by AWS’s own design, full session revocation through the console may not be immediately possible.

Overview

Zendesk announced the release of its Events Connector for Amazon EventBridge, a capability that allows engineering and customer experience teams to stream near real-time event data from Zendesk directly into Amazon Web Services (AWS). The integration targets a common pain point in customer data architecture: support interaction data that stays locked inside a CRM and never reaches the broader analytics, ML, or observability stack.

What Is the Zendesk Events Connector?

The Zendesk Events Connector is a cloud-native integration built on top of Amazon EventBridge, AWS’s serverless event bus service. It captures data-change events inside Zendesk — ticket updates, customer interactions, status changes — and publishes them to EventBridge in near real time. From there, standard EventBridge routing rules can forward those events to any supported AWS target.

Key design goals of the connector include:

• Scalability: Built on EventBridge’s serverless, highly available infrastructure, so it scales with event volume without capacity planning.
• Security: Data flows through AWS’s existing identity and access management controls.
• Low-latency delivery: Near real-time streaming means downstream systems see Zendesk state changes quickly rather than waiting for batch exports.

How Amazon EventBridge Fits In

Amazon EventBridge is a serverless event bus that decouples event producers from event consumers. Applications publish events to a bus; EventBridge applies routing rules and delivers matching events to targets such as AWS Lambda functions, Amazon Kinesis streams, Amazon S3 buckets, or third-party SaaS endpoints.

By publishing Zendesk events to EventBridge, teams can consume support data in the same pipeline architecture they already use for application events — no custom polling, webhooks, or bespoke ETL pipelines required.

Downstream AWS Services You Can Route Zendesk Events To

Once Zendesk events are on the EventBridge bus, they can be routed to any AWS service that EventBridge supports. Common use cases include:

• AWS Lambda — trigger serverless functions to automate workflows, enrich ticket data, or push updates to internal systems.
• Amazon S3 — archive raw event data for long-term storage, compliance, or batch analytics.
• Amazon Kinesis — feed a real-time analytics pipeline or a stream-processing application.
• Amazon SageMaker — supply customer interaction data to ML models for churn prediction, sentiment analysis, or routing optimization.

Zendesk Sunshine: The Broader Platform Context

The Events Connector is one component of Zendesk Sunshine, an open CRM platform Zendesk announced in November 2018. Sunshine is built entirely on AWS and is designed around open standards, giving developers the freedom to connect customer data to any tool in their stack without proprietary lock-in.

At the time of this announcement, Zendesk reported that more than 1,000 customers had already enabled the Sunshine platform. The Events Connector extends Sunshine’s data-portability mission by making event streaming a first-class, low-configuration capability rather than something that requires custom webhook infrastructure.

Real-World Integration Example: Datadog

Datadog’s technical services team is one early adopter cited at launch. By routing Zendesk events through Amazon EventBridge into Datadog’s own observability platform, the team is able to correlate customer support activity with infrastructure metrics — bringing support data into the same dashboards developers and SREs already monitor.

“Customer information shouldn’t only be used to resolve tickets, it should become a part of a company’s competitive advantage. By integrating Zendesk into Datadog through the AWS EventBridge, we are able to leverage this important source of data throughout the entire organisation in real time.”

— Dustin Lawler, VP Technical Services, Datadog

Why This Matters for DevOps and Platform Teams

Customer support data has historically been isolated from the engineering data platform. Teams building observability pipelines, data lakes, or ML feature stores often exclude Zendesk data simply because extracting it requires periodic API polling or brittle webhook setups. The EventBridge connector removes that friction:

• No custom integration code needed to get events off the Zendesk platform.
• EventBridge’s rule engine handles fan-out to multiple consumers without duplicating ingestion logic.
• IAM policies govern which AWS accounts and services can consume the events, keeping the data access model consistent with the rest of the AWS environment.
• Near real-time latency enables use cases like automatic incident correlation when a deploy causes a spike in support tickets.

Related Zendesk Product Update: Duet

Around the same period, Zendesk also released Duet, a combined offering of Zendesk Sell (sales CRM) and Zendesk Support. Duet is aimed at breaking down organisational silos between sales and service teams by presenting both pre-sale and post-sale customer interactions in a single product. While Duet is a separate offering from the Events Connector, both reflect Zendesk’s broader strategy of unifying customer data across teams and systems.

Summary

The Zendesk Events Connector for Amazon EventBridge gives engineering teams a straightforward path to include Zendesk support data in their AWS-based data platforms. By leveraging EventBridge’s serverless event routing, teams can connect Zendesk to Lambda, Kinesis, S3, SageMaker, and other services without building bespoke integration infrastructure — making customer support data a first-class input for analytics, ML, and observability pipelines.

Why AWS Skills Are in High Demand

Amazon Web Services (AWS) is one of the most widely adopted cloud platforms in the world, used by startups, mid-size companies, and Fortune 500 enterprises alike. As organisations continue migrating workloads to the cloud, demand for engineers and architects with hands-on AWS knowledge has grown steadily across roles including DevOps engineer, cloud architect, data engineer, and site reliability engineer.

Learning AWS from scratch can be overwhelming given the sheer breadth of its service catalogue — spanning compute, storage, networking, databases, machine learning, security, and developer tooling. Structured training bundles that group related courses into a coherent learning path can significantly reduce that friction.

What a Solid AWS Training Bundle Should Cover

Whether you are evaluating a bundled course package or building your own curriculum, a well-rounded AWS training programme should address the following areas:

1. Core AWS Concepts and Terminology

Before diving into specific services, learners need a firm grasp of foundational concepts: regions and availability zones, the shared responsibility model, IAM (Identity and Access Management), and the AWS Management Console. Skipping this foundation typically leads to confusion later when configuring permissions or designing multi-region architectures.

2. Compute and Storage Services

A practical AWS curriculum should cover EC2 instance types and pricing models, auto-scaling groups, Elastic Load Balancing, S3 storage classes, EBS volumes, and the basics of serverless compute with Lambda. These services appear in almost every real-world AWS architecture.

3. Networking and Security

Understanding VPCs, subnets, security groups, NACLs, and VPN or Direct Connect options is essential for anyone designing or operating cloud infrastructure. Security topics — including IAM policies, KMS encryption, CloudTrail auditing, and AWS Config — are equally critical and are heavily weighted in AWS certification exams.

4. Data Services and Analytics

For data-focused roles, training should include services such as RDS, DynamoDB, Redshift, Glue, Athena, and Kinesis. Being able to ingest, transform, query, and visualise large datasets using AWS-managed services is a highly marketable skill set in data engineering and analytics.

5. DevOps Tooling on AWS

AWS provides a native DevOps toolchain that integrates with the rest of the platform. A good training programme covers:

• CodeCommit — managed Git repositories
• CodeBuild — fully managed build service
• CodeDeploy — automated application deployment
• CodePipeline — continuous delivery orchestration
• CloudFormation — infrastructure as code using JSON or YAML templates
• Elastic Beanstalk — platform-as-a-service for rapid application deployment
• ECS and EKS — container orchestration with Docker and Kubernetes

Hands-on labs that walk through building a CI/CD pipeline end-to-end on AWS are particularly valuable for DevOps engineers transitioning from on-premises tooling.

Choosing Between Bundled Courses and Individual Certifications

Bundled training packages — often sold by platforms such as StackSocial, Udemy, or A Cloud Guru — tend to offer broad coverage at a lower upfront cost. They are a reasonable starting point for career changers or engineers exploring AWS for the first time.

However, if your goal is a specific AWS certification, it is worth checking whether the bundle content aligns with the current exam guide published by AWS. Certification exam objectives are updated periodically, and older course content may not reflect the latest service features or exam question formats.

The official AWS Training and Certification portal (aws.amazon.com/training) offers free and paid courses directly from AWS, including AWS Skill Builder, which is worth comparing against third-party bundles.

AWS Certification Paths Worth Considering

AWS organises its certifications into four levels and several speciality tracks:

• Foundational: AWS Certified Cloud Practitioner — suitable for non-technical stakeholders and beginners
• Associate: Solutions Architect, Developer, SysOps Administrator — the most common entry points for technical roles
• Professional: Solutions Architect Professional, DevOps Engineer Professional — advanced certifications for experienced practitioners
• Specialty: Data Analytics, Security, Machine Learning, Networking, and others — role-specific deep dives

Most DevOps engineers find the AWS Certified DevOps Engineer – Professional certification to be the most directly relevant, though it requires either the Developer Associate or SysOps Administrator Associate as a prerequisite.

Key Takeaways

• AWS cloud skills remain among the most in-demand competencies in the DevOps and cloud engineering job market.
• Effective AWS training should span core services, networking, security, data tooling, and DevOps-specific workflows.
• Bundled course packages can offer good value, but verify that content is current before purchasing.
• Pair any third-party training with hands-on practice in an AWS Free Tier account for the best results.
• Official AWS certification paths provide a structured, vendor-validated progression from foundational to advanced skills.

Original promotional content sourced from Engadget (engadget.com), published in partnership with StackSocial. The editorial team at BestDevOps has rewritten this post to provide independent, accurate technical guidance.

Overview

Trend Micro Incorporated (TYO: 4704; TSE: 4704) announced the extension of its network protection capabilities to the cloud through Trend Micro Cloud Network Protection, made available on AWS Marketplace. The solution was designed specifically for AWS Transit Gateway and addresses a common enterprise pain point: the operational complexity of inserting and managing network security appliances inside cloud network topologies.

The Problem: Firewall-Based Approaches in the Cloud

Traditional firewall-based network security models were built for on-premises environments and carry significant operational baggage when lifted into the cloud. According to Trend Micro, customers consistently reported three major pain points:

• Operational complexity: Managing firewall rules and routing policies across multiple cloud accounts and VPCs is labor-intensive.
• Disruptive deployment: Inserting security appliances into existing network paths often requires rearchitecting workloads or changing IP address schemes.
• Inefficiency at scale: Stateful inspection appliances that work well on-premises can become throughput bottlenecks when cloud workloads scale dynamically.

How AWS Transit Gateway Enables the Solution

AWS Transit Gateway acts as a regional network hub, centralizing routing between Amazon Virtual Private Clouds (VPCs) and on-premises networks. Instead of managing complex VPC peering meshes, network teams attach VPCs and VPN connections to a single Transit Gateway, which then controls traffic routing across all connected spokes.

Trend Micro’s Cloud Network Protection plugs into this hub model to deliver transparent inline inspection—meaning traffic is inspected as it transits the gateway without requiring changes to application configurations, routing tables within individual VPCs, or IP addressing schemes. The approach avoids the application disruption that typically accompanies inserting a security appliance into an existing network path.

FPGA Acceleration for High-Throughput Workloads

For organizations with especially demanding network traffic volumes, Trend Micro made available deployment on Amazon EC2 F1 instances, which are equipped with field-programmable gate arrays (FPGAs). FPGA-accelerated instances can offload computationally intensive packet inspection tasks from general-purpose CPUs, enabling higher throughput at lower latency—relevant for financial services, media streaming, and other high-bandwidth workloads.

Key Capabilities of Cloud Network Protection

• Transparent inline deployment: No need to rearchitect existing VPC topologies or modify application network configurations.
• Scalable inspection: Designed to scale with cloud-native elasticity, avoiding the fixed-capacity limits of traditional appliances.
• Continuous visibility: Provides ongoing monitoring of east-west and north-south traffic flowing through Transit Gateway.
• Threat intelligence integration: Leverages Trend Micro Research, including the Zero Day Initiative (ZDI) bug bounty program, to detect advanced threats, vulnerabilities, and exploits.
• Security tool integration: Designed to interoperate with existing security toolchains rather than replace them.

AWS Marketplace Availability

By listing on AWS Marketplace, Trend Micro made Cloud Network Protection discoverable and purchasable by AWS customers without requiring a separate procurement process. At the time of announcement, AWS Marketplace had approximately 230,000 active buyers, giving the solution immediate distribution reach.

“As companies move and scale their infrastructure in the cloud, they are looking for ways to easily insert and manage security appliances in their network topologies. With AWS Transit Gateway, Trend Micro’s security solution is now available to 230,000 buyers through AWS Marketplace and will help our shared customers address their network security needs in the cloud.”

— Dave Brown, Vice President, EC2 Compute & Networking Services, Amazon Web Services

“Trend Micro’s approach removes these operational challenges. With transparent deployment into the network fabric, it delivers scalable security and continuous visibility, while eliminating the need to rearchitect and avoiding application disruption.”

— Nilesh Jain, Vice President, Southeast Asia and India, Trend Micro

Customer Perspective: Copa Airlines

Copa Airlines was cited as an early customer for the solution. Alex Tarte, IT Security & Risk Manager and CISO at Copa Airlines, noted that the solution’s promise of frictionless security operations was particularly relevant for business applications that must meet the airline’s scale and performance requirements.

Why This Matters for DevOps and Cloud Network Teams

Cloud network security has historically lagged behind the pace at which DevOps teams provision infrastructure. Security appliances that require manual route table changes or static IP assignments become blockers in automated, infrastructure-as-code pipelines. A transparent inline model—where the security layer is inserted at the Transit Gateway level rather than inside individual VPC routing—aligns better with how modern cloud environments are built and operated:

• Network teams can enforce consistent inspection policies centrally, independent of individual workload owners.
• Application teams are not required to change deployment templates or Terraform/CloudFormation configurations to accommodate the security layer.
• Security posture scales automatically as new VPCs attach to the Transit Gateway, without manual intervention.

About Trend Micro

Trend Micro is a global cybersecurity vendor offering layered security for data centers, cloud environments, networks, and endpoints. The company operates in over 50 countries and runs the Zero Day Initiative (ZDI), one of the largest independent bug bounty programs, which contributes threat intelligence to its product portfolio.

From Cost Savings to Strategic Architecture

The first wave of enterprise cloud adoption had a straightforward value proposition: stop buying hardware, reduce capital expenditure, and spin up environments on demand. For many teams, this meant lifting and shifting existing workloads to AWS, Azure, or GCP with minimal re-architecture. Use cases like DevTest thrived because they required no capital investment and almost no long-term planning — spin up a test environment, validate an application, tear it down.

That approach worked until it didn’t. Organizations began running significant production workloads in the cloud and discovered they had not planned adequately for security, performance guarantees, data residency requirements, cost governance, or application portability. The early promise of cloud speed created technical and organizational debt that had to be addressed.

The second wave of cloud adoption is a direct response to those lessons. Organizations are now approaching cloud strategically: building multi-cloud architectures deliberately, refactoring applications for cloud-native deployment, optimizing spend, and standardizing on platforms that reduce vendor lock-in risk.

The Multi-Cloud Reality

Hybrid and multi-cloud are no longer aspirational states — they are the operational baseline for most large enterprises. Organizations retain on-premises infrastructure for compliance, data sovereignty, latency, security, and the simple reality of sunk capital in existing systems. At the same time, they consume public cloud services from multiple providers, either to avoid lock-in or to access best-of-breed capabilities: AI/ML platforms, managed database services, industry-specific offerings, or geographic availability zones.

This fragmentation creates a real operational challenge. Engineering and platform teams must support workloads spread across private data centers and multiple public clouds, each with its own APIs, tooling, IAM models, networking primitives, and managed service ecosystems. Without a common abstraction layer, operational complexity compounds with every new environment added.

Why Containers and Kubernetes Are the Enabling Layer

Containers and Kubernetes have emerged as the industry’s answer to multi-cloud complexity, and their widespread adoption by every major cloud provider has cemented their role as infrastructure’s common language.

What Containers Contribute

A container packages an application together with its runtime dependencies, libraries, and configuration into a portable, self-contained unit. Because the container abstracts away the underlying operating system and hardware, the same container image can run consistently on a developer laptop, an on-premises server, or any public cloud instance. This portability directly addresses the lock-in problem: if your application runs in a container, migrating it between environments becomes a scheduling and networking problem rather than a re-platforming effort.

What Kubernetes Contributes

Kubernetes provides the orchestration layer that turns individual containers into a managed, scalable, and self-healing platform. It handles scheduling (placing containers on appropriate nodes), scaling (up and down based on demand), service discovery, load balancing, rolling deployments, and failure recovery. Kubernetes also provides a declarative API model: you describe the desired state of your workloads, and Kubernetes continuously reconciles actual state toward that goal.

Critically, the Kubernetes API is consistent across environments. Whether your cluster runs on GKE, EKS, AKS, OpenShift, or a self-managed deployment on bare metal, the same kubectl commands, YAML manifests, Helm charts, and GitOps workflows apply. That consistency is what makes Kubernetes the lingua franca of multi-cloud infrastructure.

Managed Kubernetes and Hybrid Cloud Offerings

Every major public cloud provider now offers a managed Kubernetes service, removing the operational burden of running the Kubernetes control plane:

• Google Kubernetes Engine (GKE) — Google’s managed Kubernetes offering, which benefits from Google’s deep experience with container orchestration (Kubernetes originated at Google). GKE On-Prem extended this to on-premises environments.
• Amazon Elastic Kubernetes Service (EKS) — AWS’s managed Kubernetes service, complemented by AWS Outposts for on-premises deployments using AWS infrastructure and APIs.
• Azure Kubernetes Service (AKS) — Microsoft’s managed offering, paired with Azure Stack for hybrid scenarios.

On the hybrid and on-premises side, Red Hat OpenShift provides an enterprise Kubernetes distribution with a consistent operational model across on-premises clusters and public cloud deployments. IBM’s acquisition of Red Hat was explicitly framed around delivering a consistent multi-cloud platform — a recognition at the highest strategic level that container-based portability is the competitive differentiation enterprises now demand.

Choosing a Container and Kubernetes Platform

Despite the portability promise, enterprises still face real platform choices. The decision involves several dimensions:

• Managed service vs. self-managed: Managed services (GKE, EKS, AKS) reduce control-plane overhead but tie operational patterns more tightly to a provider’s ecosystem. Self-managed Kubernetes gives more control but increases operational responsibility.
• Enterprise distribution vs. upstream Kubernetes: Distributions like OpenShift bundle additional security, developer tooling, and support around upstream Kubernetes. They reduce integration work but add a vendor layer.
• On-premises footprint: Organizations with significant on-premises requirements may prefer distributions or hybrid services that treat private and public cloud as first-class peers, rather than cloud-native services with on-premises extensions bolted on.
• Operational maturity: Teams early in Kubernetes adoption benefit from managed services and opinionated distributions. Mature platform engineering teams may prefer more upstream tooling to avoid abstraction layers that limit flexibility.

The Strategic Shift in How Cloud Is Used

The underlying shift in the second cloud adoption cycle is not primarily technical — it is organizational and strategic. Engineering teams are now expected to have opinions about workload placement, cost allocation, portability requirements, and long-term vendor relationships. Platform teams are chartered to build internal platforms that abstract multi-cloud complexity from application developers. FinOps practices are emerging to govern cloud spend with the same rigor applied to capital budgets.

Containers and Kubernetes are the enabling technology layer for this strategy. They do not solve organizational complexity on their own, but they provide the portable, programmable infrastructure substrate that makes multi-cloud strategy executable rather than aspirational. Organizations that standardize on container-based workloads and a consistent Kubernetes operational model gain genuine optionality: the ability to move workloads, negotiate with providers, and adopt new services without re-platforming their entire application estate.

Key Takeaways for Platform and DevOps Teams

• Multi-cloud is the operational reality for most large enterprises, driven by compliance needs, best-of-breed provider selection, and lock-in avoidance — not just cost optimization.
• Containers provide application portability by abstracting the runtime environment from the underlying infrastructure.
• Kubernetes provides a consistent orchestration and API layer that works across on-premises and public cloud environments, making it the practical foundation for multi-cloud strategy.
• Every major cloud provider now offers managed Kubernetes and hybrid delivery options, validating containers and Kubernetes as the industry standard.
• Platform teams should evaluate managed services, enterprise distributions, and self-managed options based on on-premises footprint, operational maturity, and required portability.
• Technical platform decisions must be paired with investment in skills, processes, and governance to capture the full value of a multi-cloud architecture.