Cox Communications has launched Cox Edge, a full stack edge-cloud computing service to deliver ultra-low latency compute solutions, helping customers to improve application performance and to reduce cloud transport and on-premise infrastructure costs.

The shape of the internet is evolving. Increasingly immersive digital experiences require faster application performance and response times to delight consumers. Technologies like Internet of Things (IoT) and artificial intelligence drive the need for real-time processing and decision making. These and other innovations can benefit from bringing compute power closer to the action – to where users are engaging and to where sensor data is being created.

Cox Edge, with last mile edge locations and a global network, brings compute resources closer to users and businesses. This can help reduce cloud transport costs, improve application resiliency and continuity, reduce latency to deliver near real-time application performance, and provider better overall security.

Cox Edge is a full stack edge-cloud infrastructure service deployed from last-mile edge data centers, with offerings that will include Virtual Compute, Storage, Bare Metal, Edge CDN, Distributed Database services, Serverless computing, Distributed Containers, and Enterprise Kubernetes. The services are offered through an integrated user experience, so that developers can easily augment their cloud or on-premise deployments with edge capabilities.

What Is Mabl?

Mabl is an AI-assisted, automated testing platform focused on the front-end of web applications. Unlike traditional code-level test frameworks, Mabl tests are designed to validate user experience—verifying that an app behaves the way a real user expects, not just that the underlying code passes unit or integration checks.

As Mabl co-creator Dan Belcher explained: “Frameworks like Selenium test front-end code, not user experience. So all your scripts may pass, but from the user’s perspective the thing may still be broken.” For teams already invested in Selenium, Mabl supports importing existing Selenium test suites to ease migration.

New Integrations: GitHub, Bitbucket, and Atlassian Bamboo

The release described here focuses on extending Mabl’s reach into the daily developer workflow through integrations with popular code-hosting and CI/CD platforms. The core idea is shift-left testing—moving quality checks earlier in the development cycle so that issues are caught before they reach production or even staging.

GitHub Integration

• End-to-end Mabl tests run automatically as part of the pull request checks pipeline, blocking merges if tests fail.
• GitHub Actions can trigger Mabl test runs on commits, branch pushes, or PR events—no separate CI server required for this step.
• Tests can be snapshotted and version-pinned to specific workflow stages or product milestones, making it easier to reproduce exactly how tests behaved at a given point in time.

Bitbucket and Atlassian Bamboo Integration

• Atlassian Bamboo automation can also trigger Mabl tests, bringing front-end quality checks into teams using the Atlassian toolchain.
• Bitbucket repositories gain similar pipeline integration, allowing Mabl to slot into existing Bitbucket Pipelines workflows.

Developer-Centric Features

Beyond CI/CD pipeline integration, this release adds tooling aimed squarely at individual developers rather than dedicated QA teams.

Headless Local Test Runner

The headless local test runner lets developers execute front-end Mabl tests directly on their own machines during development—without needing to push code to a shared environment first. The runner operates in the background and surfaces issues without requiring the developer to monitor the test process manually.

Command-Line Interface (CLI)

A new CLI allows developers to configure and run Mabl tests alongside their regular development tasks. This makes it practical to run a targeted test suite from a terminal window without switching context to a browser-based dashboard.

Jira Ticket Automation for Regressions

When Mabl detects a regression or failure, it can automatically file a Jira ticket that includes:

• Full stack traces
• Screenshots or other test artifacts
• Enough context for a developer to reproduce the issue without manually re-running the test scenario

This reduces the back-and-forth between QA reporters and developers by embedding reproduction details directly in the issue tracker.

Pricing and Trial

Mabl’s pricing is available on request. At the time of this report, the platform offered:

• A 14-day free trial with full feature access
• Plans starting at 1,000 test runs per month, scaling upward based on volume and the auxiliary features required (such as cross-browser support beyond Chrome and Firefox)

Check mabl.com for current pricing, as plans and feature tiers will have changed since this article was originally published in April 2020.

Why This Matters for DevOps Teams

Integrating front-end UX testing directly into pull request workflows addresses a real gap in many CI/CD pipelines: automated tests often cover backend logic and API contracts thoroughly, but browser-rendered user flows are tested manually or only in dedicated QA sprints. By making these tests run on every commit—and by providing local and CLI tooling—Mabl reduces the cost of finding front-end regressions late in a release cycle.

The shift-left approach is especially valuable for teams practicing trunk-based development or deploying frequently, where catching a broken user flow after merge is significantly more expensive than catching it at the PR stage.

Why Team Structure Is the Foundation of DevOps Success

A report titled Full-Stack Teams, Not Engineers, published by IT Revolution and led by Jason Cox, Director of Platform and SRE at Disney, argues that DevOps “is not an organizational structure; rather, it defines a way to organize independent teams, a culture, a set of Lean principles, and a set of practices.” The report emerged from IT Revolution’s fifth annual DevOps Enterprise Forum held in Portland, Oregon.

The core thesis: the idea of the full-stack engineer is “more myth than reality.” Rather than recruiting a unicorn who can design, build, deploy, and operate software across the entire stack, organizations should instead build full-stack teams — diverse groups whose combined skills cover every phase of the software delivery lifecycle.

“Instead of hunting and waiting on the perfect full-stack engineer, a more sustainable paradigm would be for organizations to create full-stack teams. This balanced team is filled with people possessing a core set of skills plus different specialties.”

Jason Cox et al., Full-Stack Teams, Not Engineers — IT Revolution

What a Full-Stack Team Actually Looks Like

Cox and his co-authors provide two practical models for composing a full-stack team:

Model 1: Skill-Diverse Product Team

In this model, a team is assembled around a specific deliverable. The report gives the example of an internal customer health dashboard team composed of two back-end engineers, a front-end engineer, a data scientist, a data analyst, a designer, and a product manager. Each member is essential, but rotation between roles and cross-teaching within the team are strongly encouraged. Members also participate in communities of practice across teams to build and share deeper expertise.

Model 2: Embedded Functional Experts

In the second model, functional specialists are embedded directly into product teams. Team members carry two reporting relationships: a primary one to the product team leader, and a secondary one to their functional area manager, who is responsible for skill development and career progression. Embedded team members take ownership of both delivery and operational reliability while also ensuring the wider team gains literacy in their specialty.

4 Ways to Energize Your DevOps Teams

Beyond team composition, culture and physical environment play a significant role in DevOps effectiveness. A second IT Revolution report introduces the concept of the Dojo — from the Japanese for “place of the way” — as a transformation immersion center for DevOps teams. The concept is outlined by Ross Clanton, Executive Director of Technology Modernization at Verizon, and his co-authors.

Target’s CIO Mike McNamara describes how the Dojo model worked in practice at their organization:

“The Dojo is an immersive, six-week session where teams execute their normal work with Agile coaches on site to support them and provide anything they need from a DevOps point of view. The Dojo has been fantastic in getting teams engaged with Agile and DevOps, removing the natural resistance and fear of change, and then supporting the team through the changes while maintaining productivity. It’s been a huge success for Target.”

Mike McNamara, CIO — Target

Clanton and his co-authors outline four concrete steps to establish a Dojo that energizes DevOps initiatives:

1. Create a Space for Learning and Fun

Place the Dojo in a high-traffic area of the office. Visibility broadens exposure to what teams are experiencing, creates curiosity across the organization, and drives future demand for Dojo services. The physical space should feel open, inspiring, and welcoming — not another grey conference room.

2. Arrange Team Tables That Optimize Collaboration

Seating layout matters. Options include individual desks grouped together with dual monitors at each station, or a “family-style” table with no monitors to enable free-flowing conversation. The governing principle: everyone should sit in close proximity so that in-person collaboration becomes the path of least resistance. Whiteboards are essential for visualizing ideas and maintaining shared situational awareness.

3. Design Open Spaces That Enable Inter-Team Collaboration

Silos dissolve when teams can learn from each other organically. Some of this happens naturally — teams overhear adjacent conversations that happen to be relevant to their own problems. But open spaces also create intentional networking opportunities, allowing teams to identify where they can support each other and surface shared problems earlier.

4. Set Up a Communication and Collaboration Hub

Clanton and his co-authors recommend creating a “demo lounge” — a dedicated area equipped with a projector or large display surface, comfortable and informal seating, sound amplification, and video conferencing capability to connect with other Dojos or remote teams. The demo lounge gives all Dojo teams a venue to share their work, and gives stakeholders — management, business partners — a natural entry point to observe progress without disrupting the team’s flow.

Mastery Takes Time — Teams Make It Possible

As Cox and his co-authors note in the Full-Stack Teams report, “it takes years of hard work to become a master of anything, and DevOps skills are no different.” No single engineer can realistically own the entire DevOps landscape. The organizations that consistently deliver software at speed and scale are those that invest in assembling, developing, and sustaining diverse teams — and in creating the environments that let those teams do their best work.

Whether through the embedded-expert model, the skill-diverse product team, or an immersive Dojo programme, the underlying principle is the same: DevOps culture lives in teams, not individuals. Getting that structure right is the prerequisite for everything else.

Source: Originally reported by ZDNet. Reports referenced: Full-Stack Teams, Not Engineers and the Dojo report, both from IT Revolution’s DevOps Enterprise Forum series.

Dynatrace Brings Full-Stack Observability to Kubernetes

At its Perform 2020 conference in Las Vegas, Dynatrace announced a set of enhancements to its Kubernetes support, centred on deeper integration with its AI engine, Davis, and broader coverage for container runtimes. The updates are aimed at teams operating large, heterogeneous Kubernetes environments who need automated, context-aware observability rather than manual metric-dashboard inspection.

Davis AI Now Automatically Ingests Kubernetes Metrics and Events

The headline capability is that the Davis AI engine now automatically ingests additional Kubernetes events and metrics. According to Dynatrace, this allows Davis to deliver real-time, precise answers about performance issues and anomalies across the full Kubernetes stack — from clusters and nodes down to individual containers and the workloads running inside them.

Because Davis ingests dependency and relationship data across the entire stack automatically, it can correlate a latency spike in a microservice with, for example, resource contention at the node level — without requiring an operator to manually stitch together data from separate dashboards.

Steve Tack, SVP of Product Management at Dynatrace, framed the problem this way:

“As organisations scale up their Kubernetes applications and microservices in production, the dynamic and distributed nature of these environments makes it impossible to understand and manage simply by looking at metrics and dashboards.”

Tack added that Dynatrace was reinvented specifically to support multi-cloud and microservices architectures, and that Davis AI’s automatic ingestion of Kubernetes metrics is what distinguishes the platform as a full-stack observability solution rather than a metrics aggregator.

Automatic Discovery and Mapping of Heterogeneous Container Runtimes

A significant operational improvement in this release is automatic discovery, instrumentation, and topology mapping of heterogeneous container technologies running inside Kubernetes. Dynatrace now supports the following container runtimes out of the box:

• Docker — the most widely deployed container runtime
• CRI-O — the lightweight OCI-compliant runtime developed for Kubernetes
• containerd — the CNCF-graduated runtime now the default in many managed Kubernetes distributions

This matters in practice because large enterprise Kubernetes environments often mix container runtimes across node pools — particularly when migrating workloads or running managed clusters on different cloud providers. Previously, non-Docker runtimes required additional manual instrumentation or provided limited visibility. With automatic discovery across all three runtimes, Dynatrace claims operators can maintain consistent observability regardless of the underlying runtime.

Container Resource Usage Analysis and Kubernetes Resource Optimisation

Dynatrace also introduced new cloud application and microservice analysis capabilities focused on Kubernetes resource utilisation. These tools allow administrators and application owners to:

• Understand how CPU and memory are consumed across pods and namespaces
• Identify over-provisioned or under-provisioned workloads
• Detect performance degradation linked to resource contention
• Solve issues faster by correlating resource usage with application-layer symptoms

The new container resource usage analysis also broadens coverage to match the range of runtimes described above, ensuring that resource metrics are available uniformly regardless of how containers are executed on the node.

Why This Matters: The Scale of Kubernetes Adoption

Dynatrace cited a CIO research report at the time of the announcement, claiming that 68% of organisations were already using containers, with 86% expecting to deploy them within twelve months. While these figures were current as of early 2020 and adoption has continued to grow since, they illustrate the scale of the operational challenge: most engineering organisations are running containers in production, and a large proportion are converging on Kubernetes as their orchestration layer.

At that scale, manual observability — watching dashboards, correlating alerts by hand, tracing issues across dozens of microservices — becomes untenable. The case for AI-assisted, automatic full-stack observability is straightforward: the environments are too dynamic and too distributed for human operators to reason about without tooling that understands dependencies in real time.

Key Takeaways for Platform and DevOps Engineers

• Automatic metric ingestion reduces toil: Davis AI’s ability to ingest Kubernetes events and metrics without manual configuration lowers the instrumentation burden for platform teams.
• Multi-runtime support is increasingly important: As containerd and CRI-O displace Docker as default runtimes in major distributions, observability tools must keep pace — and this announcement signals that Dynatrace is tracking that shift.
• Resource optimisation is a day-two concern: The addition of Kubernetes resource utilisation analysis addresses a common post-migration pain point: understanding why a cluster costs what it costs and where workloads are underperforming.
• Full-stack correlation, not just metrics: The value proposition here is not raw metric collection but the causal chain from infrastructure event to application symptom — the AI layer is what makes that connection.

For teams evaluating Kubernetes observability platforms, the Dynatrace Perform 2020 announcements represent a meaningful expansion of automated coverage — though, as with any vendor announcement, independent evaluation against your own workload patterns and runtime choices remains essential.

Why Cloud Computing Skills Matter

Cloud computing has become a core competency for modern IT organisations. Platforms from Amazon, Google, IBM, and Microsoft now underpin the infrastructure of enterprises across every sector, and demand for certified cloud professionals continues to outpace supply. Whether you are an experienced IT professional looking to pivot or a developer adding cloud-native skills, formal training and certification can significantly accelerate your career trajectory.

The following ten programmes represent a range of options — from postgraduate certificates to self-paced nanodegrees — available to learners based in India. This is not a ranking. Pricing and curriculum details reflect what was publicly available in late 2019 and should be treated as approximate; always confirm current information with the provider.

1. Jigsaw Academy (in association with Manipal Academy of Higher Education)

PG Certificate Program in Cloud Computing

• Duration: 6 months
• Cost: ₹88,500
• Delivery: Online (lab access included)
• Cities: Bangalore

Jigsaw Academy operates as part of the Manipal Education group and focuses on industry-relevant professional certification. This postgraduate certificate programme claims to be one of the only offerings in the market to include a dedicated lab experience for every task performed on cloud platforms.

The programme includes 300+ hours of online learning, with 70+ hours of live online lectures delivered on weekends by industry experts. Courseware is aligned with AWS Educate and covers Cloud Essentials, Cloud Foundry, Google Cloud, and OpenStack, with a specialisation track in AWS. Learners can use the programme to prepare for certifications including AWS Solutions Architect – Associate, AWS Developer – Associate, and AWS Certified SysOps Administrator.

2. Great Learning

Post Graduate Program in Cloud Computing (PGP CC)

• Duration: 6 months
• Cost: ₹1,25,000 + GST
• Delivery: Online

Great Learning is an ed-tech platform covering analytics, data science, machine learning, AI, cloud computing, DevOps, and full-stack development. Its PGP CC is designed to give candidates hands-on exposure to core cloud technologies including containers, microservices, cloud security, DevOps practices, cloud architecture, migration, and deployment — taught across AWS, Azure, and Google Cloud.

A notable differentiator is access to mentorship from industry professionals working at companies such as Dell, Microsoft, and Adobe. Group projects and peer interaction with cohorts that average 8+ years of professional experience are also part of the learning structure. Career guidance and interview preparation are included.

3. Simplilearn

Cloud Architect Master’s Program

• Duration: 352 hours (live virtual classes plus self-paced learning)
• Cost: ₹44,999
• Cities: Bangalore, Hyderabad, Pune, Mumbai, Gurgaon, Noida, and select international locations

Founded in 2010 and headquartered in San Francisco and Bangalore, Simplilearn offers outcome-based online training across digital technologies. Its Cloud Architect programme focuses on AWS and Microsoft Azure, covering the skills needed to design and deploy scalable, highly available, fault-tolerant, and reliable cloud applications.

Key topics include AWS CloudFormation, Azure Resource Manager, EC2, S3, Route 53, VPC, and Azure App Services. Completion prepares learners for cloud architect, cloud developer, and cloud engineer roles, as well as the associated vendor certification exams.

4. Coursera

Google Cloud Platform Fundamentals: Core Infrastructure

• Cost: Free enrolment (certificates may carry a fee)
• Delivery: Online (global)

Founded in 2012 by Stanford professors Daphne Koller and Andrew Ng, Coursera partners with 190+ universities and companies — including Yale, University of Pennsylvania, Google, and IBM — to deliver online courses, specialisations, and degrees.

This introductory GCP course covers key services including Google App Engine, Compute Engine, Kubernetes Engine, Cloud Storage, Cloud SQL, and BigQuery. It also introduces resource and policy management using Cloud Resource Manager and Cloud IAM. Hands-on labs provide practical exposure to GCP fundamentals.

Credits from this course can be applied toward several Coursera specialisations and professional certificate programmes, including:

• Developing Applications with Google Cloud Platform Specialisation
• Architecting with Google Kubernetes Engine Specialisation
• Cloud Engineering with GCP Professional Certificate
• Architecting with Google Compute Engine Specialisation
• Cloud Architecture with GCP Professional Certificate
• Networking in Google Cloud Platform Specialisation
• Security in Google Cloud Platform Specialisation

5. Udacity

Cloud Developer Nanodegree

• Cost: ₹22,299 per month (at time of publication)
• Delivery: Online (global)

Started as an experiment in online learning by Stanford instructors Sebastian Thrun and Peter Norvig, Udacity focuses on career advancement through mastery of in-demand technical skills. Its Cloud Developer Nanodegree begins with cloud fundamentals and AWS deployment, then progresses through building full-stack apps, leveraging microservices, Kubernetes clusters, and serverless architectures.

Core modules include:

• Cloud Foundations
• Full-Stack Apps on AWS
• Monolith to Microservices at Scale
• Development and Deployment of a Serverless Application

6. SpringPeople

Google Cloud Platform (GCP) for Developers Training

• Duration: 3 days (classroom) or 18 hours (live online)
• Cost: ₹19,995
• Cities: Bangalore, Delhi NCR, Gurgaon

Founded in 2009 by IIT alumni, SpringPeople is an enterprise IT training provider with 500+ courses. Its GCP for Developers course is practical and developer-focused, covering application development best practices, GCP client libraries, the Cloud SDK, Firebase SDK, and data storage options.

Specific topics include Cloud Datastore, Cloud Storage, Cloud Pub/Sub, Cloud Functions, Cloud Endpoints, Cloud Build, Container Registry, and performance monitoring with Google Cloud Operations (formerly Stackdriver). Training uses CloudLabs for hands-on practice, and real-time code analysis and feedback is a stated feature of the programme.

7. Edureka

AWS Architect Certification Training

• Duration: 15 days (weekday batch) or 5 weeks (weekend batch)
• Cost: ₹19,995
• Delivery: Online

Edureka is a global e-learning platform offering live instructor-led training with 24×7 lifetime support. Its AWS Architect Certification Training is designed around the AWS Certified Solutions Architect (CSA) exam and covers AWS architectural principles, scalability, reliability, and high availability.

Practical components include signing up for an AWS free-tier account, creating an S3 bucket via the Console and AWS CLI, and launching an EC2 instance. The course is geared toward IT professionals who want to qualify as an AWS Certified Solutions Architect.

8. edX

CS50 for Business Professionals (Harvard University)

• Duration: 6 weeks
• Cost: Free audit; ₹6,500 for a verified certificate
• Delivery: Online (global)

edX is a non-profit e-learning platform founded by MIT and Harvard, offering courses from 140+ institutions worldwide including Oxford, Columbia, and Berkeley. It provides free audit options and affordable certifications, and its MicroMasters programmes allow learners to stack credentials toward formal degrees.

The Harvard CS50 for Business Professionals course takes a top-down approach to computer science, emphasising high-level concepts and design decisions rather than low-level implementation. It is designed for managers, product managers, founders, and decision-makers. Topics covered include:

• Computational thinking
• Programming languages
• Internet technologies
• Web development
• Technology stacks
• Cloud computing

9. Inventateq

Introduction to Cloud Computing

• Duration: 2 months
• Cost: ₹15,000
• Cities: Bangalore and Chennai

Inventateq is a software training company focused on job-ready IT education. Its cloud computing course covers foundational concepts, cloud deployment and service models, and an introduction to AWS — including key vocabulary, services, and architectural concepts.

A notable commercial differentiator is its stated 100% job placement guarantee, backed by partnerships with 500+ IT companies. Trainees reportedly receive unlimited interview calls until placement is achieved, along with practical, domain-specific training.

10. CloudThat

All Access Learning Pass (AALP)

• Duration: 1 year
• Cost: ₹99,000
• Cities: Bengaluru, Mumbai, USA, UK

Established in 2012, CloudThat provides cloud training and consulting services for mid-market and enterprise clients. It holds expertise across AWS, Microsoft Azure, and Google Cloud Platform, and also covers big data and IoT.

The All Access Learning Pass is a subscription-style offering that allows IT professionals to attend any CloudThat course for one year. Learners can freely choose across technologies including AWS, Azure, Google Cloud, IoT, DevOps, blockchain, machine learning, and big data. The pass includes dedicated training coordinators, resume building support, and job placement assistance.

Choosing the Right Cloud Computing Course

When evaluating these or any cloud training programme, consider the following factors:

• Certification alignment: Does the programme map to a recognised vendor certification (AWS, Azure, GCP)?
• Hands-on labs: Theory alone is insufficient for cloud roles — look for programmes with practical lab components on real cloud environments.
• Delivery format: Self-paced, live online, and in-person classroom formats suit different learning styles and schedules.
• Cost vs. depth: Lower-cost introductory courses and free MOOCs are valuable for beginners, but postgraduate programmes offer more structured depth for career changers.
• Career support: Placement assistance, mentorship, and interview preparation vary significantly across providers.
• Currency of content: Cloud platforms evolve rapidly. Always verify that the curriculum is current before enrolling.

All pricing, course details, and availability information in this article reflects what was published in late 2019. Contact each provider directly for current offerings.