A DevOps Engineer plays a critical role in ensuring that development, operations, and IT teams work together effectively to deliver high-quality software in a fast and efficient manner. The daily work of a DevOps Engineer involves a mix of tasks, including automation, system monitoring, incident resolution, and collaboration with various teams to improve the overall software lifecycle.

In this post, we will explore the typical daily tasks of a DevOps Engineer, shedding light on the responsibilities they handle and the tools they use to ensure smooth software development and operations.

1. Managing CI/CD Pipelines

Major Responsibilities:

• Monitor Continuous Integration (CI):
  • DevOps Engineers ensure that continuous integration (CI) pipelines are running smoothly. This involves checking the automated build and test processes, verifying that code changes integrate correctly, and catching any issues early in the development cycle.
• Manage Continuous Deployment (CD):
  • They oversee continuous delivery (CD) pipelines, ensuring that code changes automatically flow through testing and deployment environments. DevOps Engineers make sure that deployments happen quickly and reliably without breaking production systems.
• Automate Testing and Deployment:
  • A key task is automating repetitive tasks like testing, integration, and deployment. DevOps Engineers typically work with Jenkins, GitLab CI/CD, CircleCI, or other tools to automate these processes.
• Troubleshoot Pipeline Failures:
  • If there are any pipeline failures, DevOps Engineers dive into logs and metrics to troubleshoot and resolve issues, ensuring that code can be deployed quickly and with minimal manual intervention.

2. Infrastructure Management and Automation

Major Responsibilities:

• Provision and Manage Infrastructure:
  • DevOps Engineers are responsible for managing infrastructure in both on-premise and cloud environments (AWS, Azure, Google Cloud). They ensure that the underlying infrastructure is properly provisioned, configured, and scalable to meet the needs of the applications.
• Infrastructure as Code (IaC):
  • DevOps Engineers write and maintain Infrastructure as Code (IaC) scripts using tools like Terraform, AWS CloudFormation, or Ansible to automate the creation and management of infrastructure resources.
• Cloud Services and Virtual Machines:
  • They regularly interact with cloud services, managing virtual machines, containers, and databases. Automation is key to ensuring that the infrastructure is consistent across environments, minimizing configuration drift.
• Ensure Cost Efficiency:
  • DevOps Engineers also monitor resource utilization, helping to optimize cloud spending by scaling down unused resources or using reserved instances to reduce costs.

3. System Monitoring and Incident Management

Major Responsibilities:

• Real-Time Monitoring of Systems:
  • DevOps Engineers set up and maintain real-time monitoring systems using tools like Prometheus, Grafana, Datadog, or the ELK Stack. They track application performance, system health, and infrastructure metrics to ensure everything runs smoothly.
• Incident Response and Troubleshooting:
  • When issues arise in the system, such as performance degradation or downtime, DevOps Engineers are the first to respond. They troubleshoot and resolve incidents to minimize disruption, often using log aggregation tools like Splunk or ELK Stack to diagnose issues.
• Root Cause Analysis:
  • After resolving incidents, DevOps Engineers perform root cause analysis (RCA) to understand the underlying issues and implement corrective actions to prevent recurrence. This could involve fixing bugs, optimizing code, or improving system architecture.
• Alert Management:
  • They configure alerting systems that notify teams of potential issues, allowing for quick resolution. Ensuring that the right people receive alerts at the right time is a crucial task in maintaining system uptime.

4. Collaboration with Development and Operations Teams

Major Responsibilities:

• Facilitate Cross-Functional Communication:
  • DevOps Engineers work as a bridge between the development (Dev) and operations (Ops) teams, fostering communication and collaboration. They ensure that developers have the tools and resources needed for efficient coding, testing, and deployment.
• Support Developers in CI/CD Process:
  • They help development teams integrate new code into the system by ensuring that CI/CD pipelines are correctly set up. DevOps Engineers work closely with developers to optimize deployment workflows and streamline the overall process.
• Collaborate on Automation Projects:
  • They collaborate with various teams to automate manual processes, such as deployments, environment configuration, and scaling. Their goal is to reduce friction and improve efficiency across the development and operations workflows.
• Provide Technical Guidance:
  • DevOps Engineers often provide guidance on best practices for system design, cloud usage, and automation. They act as technical mentors for other team members, helping them optimize the deployment and infrastructure management process.

5. Security and Compliance

Major Responsibilities:

• Implement DevSecOps Practices:
  • DevOps Engineers work to integrate security practices into the software delivery pipeline, a practice known as DevSecOps. This includes ensuring that security is baked into CI/CD processes, performing code scanning for vulnerabilities, and automating security testing.
• Ensure Compliance:
  • They also ensure that the deployed applications comply with industry regulations such as GDPR, HIPAA, or PCI-DSS. This includes setting up security monitoring, access control, and audit trails.
• Access Control Management:
  • DevOps Engineers manage access control policies to ensure that only authorized personnel can access critical systems and data. They implement tools like Vault or AWS IAM to manage secrets and credentials securely.
• Proactive Security Audits:
  • They perform regular security audits of infrastructure and software deployments, ensuring compliance with security best practices. This includes using tools like Snyk or Aqua Security to scan for vulnerabilities.

6. Continuous Improvement and Optimization

Major Responsibilities:

• Evaluate System Performance:
  • DevOps Engineers constantly evaluate system performance and identify bottlenecks in the infrastructure. They use tools like Grafana or New Relic to monitor application performance and optimize performance across various services.
• Automate Repetitive Tasks:
  • One of their primary objectives is to automate repetitive tasks to reduce manual intervention. This could include automating database backups, patch management, and scaling applications based on usage patterns.
• Scaling Systems:
  • DevOps Engineers ensure that systems are scalable, particularly during peak demand periods. They configure auto-scaling mechanisms using tools like Kubernetes or AWS Auto Scaling to handle changes in traffic or resource needs.
• Optimize Resource Usage:
  • DevOps Engineers regularly monitor resource consumption and optimize usage to reduce costs and improve efficiency. This includes optimizing cloud infrastructure to avoid overprovisioning and ensuring that resources are used efficiently.

A Day in the Life of a DevOps Engineer

The daily work of a DevOps Engineer is dynamic, challenging, and multifaceted. From managing CI/CD pipelines to automating infrastructure, responding to incidents, and improving system performance, the role is central to the efficient delivery of software and the reliability of IT operations.

DevOps Engineers serve as a crucial link between development, operations, and security teams, ensuring that software is developed, deployed, and maintained quickly, securely, and reliably. As organizations continue to adopt DevOps practices, the demand for skilled DevOps Engineers continues to grow, making this a vital and rewarding career path.

In the world of IT, DevOps and IT Operations (ITOps) are two critical concepts that shape the way organizations develop, deploy, and manage software applications. While both aim to enhance the efficiency and reliability of IT systems, they focus on different aspects of the software lifecycle and have distinct goals. Understanding the differences between these two approaches is crucial for organizations looking to optimize their IT infrastructure and development workflows.

In this post, we will explore the key differences between DevOps and ITOps, outline their major features, and highlight how they work together to support high-performing IT environments.

1. Definition and Scope

DevOps:

• Collaborative Culture:
  • DevOps is a set of practices and a cultural philosophy that aims to unify and automate the work of software development (Dev) and IT operations (Ops). It emphasizes collaboration, communication, and integration between traditionally separate teams.
• Focus on Continuous Delivery:
  • DevOps focuses on speeding up the software development and delivery process by automating continuous integration (CI), continuous delivery (CD), and continuous testing. The goal is to shorten the development lifecycle, deliver features faster, and maintain high software quality.
• End-to-End Involvement:
  • DevOps teams are involved throughout the software lifecycle, from planning and development to testing, deployment, and monitoring in production. This end-to-end involvement allows for faster feedback and iteration.

ITOps:

• Traditional IT Management:
  • ITOps focuses on managing and maintaining an organization’s IT infrastructure, including servers, networks, databases, and other hardware and software systems. It is primarily concerned with keeping IT systems running smoothly, secure, and reliable.
• Operational Excellence:
  • ITOps is focused on optimizing the performance, availability, and reliability of IT services. It deals with tasks like system monitoring, security patches, backups, and troubleshooting.
• Reactive and Proactive Management:
  • ITOps teams address issues both reactively (e.g., fixing problems as they arise) and proactively (e.g., anticipating and preventing potential issues). It’s about maintaining the health of the IT infrastructure on an ongoing basis.

2. Focus Areas and Objectives

DevOps:

• Accelerating Software Delivery:
  • The primary focus of DevOps is to accelerate the delivery of software applications by fostering collaboration between development and operations teams. By automating repetitive tasks and integrating CI/CD pipelines, DevOps enables faster software releases and higher-quality applications.
• Automation and Efficiency:
  • DevOps emphasizes automation in areas like infrastructure provisioning, configuration management, testing, and deployment. Automation increases efficiency, reduces human error, and accelerates the software delivery process.
• Collaboration and Communication:
  • DevOps is built around the idea of breaking down silos between development and operations teams. This collaboration ensures better communication, faster decision-making, and a smoother workflow throughout the development cycle.

ITOps:

• Infrastructure Management and Optimization:
  • ITOps is centered around the management of IT infrastructure. Its focus is on ensuring that systems, servers, networks, and databases operate efficiently and without disruption. The goal is to provide stable and secure infrastructure to support applications and users.
• Service Continuity and Availability:
  • A key goal of ITOps is ensuring that critical IT services remain available and secure. This involves monitoring system health, preventing outages, ensuring compliance with security policies, and handling capacity planning.
• Incident and Problem Management:
  • ITOps teams are responsible for responding to incidents (e.g., system failures or downtime) and ensuring the underlying infrastructure is running smoothly. They work to minimize downtime and optimize the system’s performance.

3. Automation and Tools

DevOps:

• CI/CD Tools:
  • DevOps heavily relies on CI/CD tools like Jenkins, GitLab, CircleCI, and Travis CI to automate the process of building, testing, and deploying applications. This automation speeds up the software release cycle and reduces the chances of human error.
• Infrastructure as Code (IaC):
  • Tools like Terraform, Ansible, and AWS CloudFormation are used to automate infrastructure management through code. This ensures consistency across environments and improves the scalability of applications and infrastructure.
• Monitoring and Logging:
  • DevOps teams use tools like Prometheus, Grafana, and the ELK Stack to monitor applications and infrastructure. This helps detect issues early, ensuring quick resolution and improving system performance.

ITOps:

• System Monitoring and Alerts:
  • ITOps uses traditional system monitoring tools like Nagios, Zabbix, and SolarWinds to track server health, network traffic, and overall infrastructure performance. These tools alert the team to any potential problems.
• Configuration Management:
  • ITOps teams use configuration management tools such as Puppet, Chef, and SaltStack to ensure consistency in server configurations, software installations, and security settings.
• Patch Management and Security:
  • ITOps relies on security and patch management tools, such as WSUS (Windows Server Update Services) or Red Hat Satellite, to ensure systems are up to date-and protected from vulnerabilities.

4. Collaboration and Responsibilities

DevOps:

• Cross-Functional Collaboration:
  • DevOps emphasizes a collaborative approach where development, testing, and operations teams work together throughout the software lifecycle. This ensures faster feedback, better code quality, and quicker resolution of issues.
• Shared Accountability:
  • In DevOps, teams are jointly responsible for both the success and failure of applications. Development teams are accountable for code quality and functionality, while operations teams focus on infrastructure reliability and performance.
• Continuous Feedback:
  • DevOps encourages continuous feedback loops, where performance data, user feedback, and incident reports are shared regularly across teams to improve the development process and user experience.

ITOps:

• Specialized Roles:
  • ITOps is typically more specialized, with teams focusing on specific aspects of IT infrastructure management, such as networking, server maintenance, security, and cloud management. These roles are separate from the development teams.
• Siloed Responsibilities:
  • In traditional ITOps environments, there may be a lack of collaboration between development and operations teams. However, some modern ITOps models are beginning to incorporate more cross-functional collaboration to align with DevOps practices.
• Incident Management:
  • ITOps is responsible for resolving incidents and minimizing downtime. Their focus is on maintaining system uptime, security, and troubleshooting issues in the infrastructure that might impact the application’s performance.

5. Impact on Development and Operations

DevOps:

• Faster Time-to-Market:
  • By automating workflows and improving collaboration, DevOps accelerates the software development process, allowing organizations to release software faster and more frequently.
• Improved Quality and Reliability:
  • Continuous testing, integration, and monitoring ensure that code is thoroughly tested before it reaches production, reducing the likelihood of bugs and performance issues in the final product.
• Scalable and Agile Development:
  • DevOps fosters a more agile environment, where developers can quickly iterate on new features, make changes, and scale applications based on feedback and demand.

ITOps:

• Operational Stability and Continuity:
  • ITOps focuses on maintaining the stability and continuity of IT services. It ensures that all infrastructure components are functioning properly and that services are available to meet business needs.
• Risk Mitigation:
  • ITOps teams play a key role in mitigating risks related to system downtime, security vulnerabilities, and compliance violations. They ensure that infrastructure is secure and compliant with industry standards.
• Cost Optimization:
  • ITOps teams often work to optimize resource utilization and manage costs related to IT infrastructure. They may oversee cloud cost management, server optimization, and capacity planning to ensure cost-effective operations.

DevOps vs ITOps – Complementary Practices

While DevOps and ITOps serve different roles in software development and IT management, they are complementary. DevOps emphasizes collaboration, automation, and the rapid delivery of software, while ITOps focuses on managing and maintaining the underlying IT infrastructure that supports those applications. Both are critical for creating efficient, high-performance IT environments.

Organizations that successfully integrate both DevOps and ITOps practices can benefit from faster software releases, stable and reliable infrastructure, and a collaborative work environment that drives continuous improvement across teams.

Unisys has announced it has joined Google’s Cloud Partner Advantage programme.

By providing Google’s complete suite of Cloud and Infrastructure services and Digital Workplace Services, Unisys says it can offer clients more options for optimising their digital workplace environments through its’ end-to-end cloud management services.

In addition to joining the programme, Unisys will sell Google Cloud and Google Workspace solutions as part of its offering.

“This partnership announcement with Google is significant because it allows Unisys to expand our existing cloud-agnostic approach in Asia Pacific,” says Brad Bowden, director cloud & infrastructure, Unisys Asia Pacific,

“Strong vendor relationships are key to Unisys being able offer the best solutions to the client business transformation challenge,” he says.

“The success of our go-to-market strategy is directly related to the investment to partner with market leaders like Google.”

According to the company, joining the Google Cloud Partner Advantage Program expands Unisys’ ability to deliver outcome-based, end-to-end cloud services to its clients via a complete array of cloud advisory, digital transformation, application modernisation, migration, security, compliance automation, orchestration and comprehensive managed services capabilities.

Further, as a Google Workspace reseller, Unisys will assist its clients using Google Cloud to optimise their workplace environment through its extensive field services, service desk, endpoint management, productivity and collaboration offerings.

In February, Unisys was named as a Microsoft Azure Expert Managed Service Provider (MSP) for the third year in a row.

To pass the certification, Unisys had to demonstrate that it had worked successfully with its Azure clients on its Cloud Management Platform (CMP), which is powered by its CloudForte technologies.

Unisys vice president cloud and infrastructure, Mike Morrison, said at the time, “By again passing the Azure Expert MSP criteria, Unisys has proven that it can consistently and securely design, implement and manage Azure services.

“Our success with the clients for whom we are currently using AI-led automation, migrations, optimisation and security to realise the benefits of their cloud transformation is even more concrete proof.”

That same month Unisys joined the Lenovo partner ecosystem.

Unisys says its support of Lenovo’s solutions will be backed by its Digital Workplace Services, including pre-deployment, deployment, maintenance and lifecycle management supply chain services.

Lenovo president of commerical IoT group John Gordon said at the time: “Our ThinkIoT Solutions are helping to accelerate business transformation for the long-term.

The Global “ Container Infrastructure Software Market” 2021 Research report produces information with reference to market size, share, trends, growth, cost structure, capacity, revenue, and forecast 2026. This report also contains the general and comprehensive study of the Container Infrastructure Software market with all its aspects influencing the growth of the market. This report is a full-scale quantitative survey of the Container Infrastructure Software industry and provides data for building strategies to expand the market growth and effectiveness.

Executive summary:

The research report on Container Infrastructure Software market comprises a thorough evaluation of the past and present business scenario to deliver a conclusive overview of the industry’s growth patterns over 2021-2026. It casts light on the sizes and shares the markets and sub-markets, emphasizing on the primary growth stimulants, obstacles, and lucrative prospects impacting the business dynamics.

As per expert analysts, Container Infrastructure Software market is predicted to record notable growth over the study period, bolstering at XX% CAGR throughout.

The research literature also includes actionable insights into the competitive landscape, stressing on the well-known strategies employed by major contenders to enjoy strong profit margins in the upcoming years. In addition, the document answers all queries relating to implications of COVID-19 pandemic.

Market snapshot:

Geographical outlook:

Key regional contributors of Container Infrastructure Software market are Americas, APAC, Europe, Middle East & Africa.
Market share in relation to the consumption volume and value of each regional market is taken into account.
Role of key regions to the overall industry progression is computed.
Product landscape outline:

The document segments the product terrain of Container Infrastructure Software market into Mobile Phone , Tablet and Computer.
Industry share of every product segment is given.
Data relating to overall revenue generated and total sales amassed by every product type are provided.
Application spectrum summary:

The application spectrum of Container Infrastructure Software market is bifurcated into Monitoring and Logging , Security , Continuous Deployment , Management and Orchestration , Networking and Data Management Services and Others.
Estimates for consumption volume and value of every application scope are supported with stats.
Approximations for each application segment’s market share over the analysis period are enumerated as well.
Competitive arena overview:

Top companies in Container Infrastructure Software market is Dell EMC , HPE , Microsoft , Broadcom , Canonical , Oracle Corporation , Cray , Cavium , Docker , Cisco , Mellanox , SUSE , Supermicro , IBM , Intel , Inspur , WiWyn , Lenovo , Huawei and White Box QCT.
The document offers a business overview of all the mentioned players.
Records of net revenue, pricing models, operating profits, sales, and other financial attributes of each organization are systematically sorted.
Manufacturing facilities and operating areas of the listed players are given.
Up-to-date data on partnerships, associations, and new entrants in the domain are also presented in the study.

What will be the Container Infrastructure Software market scale and growth rate in the estimate years from 2021 to 2026?
What are the significant driving factors of the Container Infrastructure Software market?
What are the opportunity and challenges in front of the market?
Who are the key vendors in the Container Infrastructure Software market?
What are the trending features manipulating market equity?
What are the key products of Porter’s five forces model?
what are the global prospects for expanding the global Container Infrastructure Software market?
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Discussions about the relationship between cloud computing and edge computing have a tendency to present the cloud and edge as opposite types of architectures and to treat them as an either/or proposition.

This isn’t the best way to think about cloud and edge. Although there are key differences between cloud and edge computing, the cloud and the edge don’t compete with each other as much as they complement each other.

Differences between cloud and edge
The differences between cloud computing and edge computing boil down largely to where workloads are hosted.

In a conventional cloud, data and applications are housed in large data centers located within a public cloud vendor’s network.

In an edge architecture, workloads are hosted in a location that is “closer” — from the perspective of the network — to end-users than is a traditional data center.

Multiple approaches to edge computing
It’s worth noting that the definition of edge can get a little messy because there are many possible ways to build an edge architecture. Sometimes, an edge environment runs directly on end-user devices. In others, it looks more like a traditional data center populated by conventional servers, which happen to be closer to end-users than traditional cloud data centers.

Cloud computing tends to be more straightforward from an architectural perspective. Although there can be some variation in which cloud regions and zones teams use to host workloads, all public cloud environments rely on conventional data centers that are comparatively distant — again, in networking terms — from end-users.

Why cloud and edge computing go together
Although cloud and edge computing are fundamentally different types of architectures, “cloud vs. edge” is not necessarily the best way to think about them.

In many cases, it makes sense to use both cloud and edge at the same time. Cloud data centers can host workloads that don’t require the minimal latency and high reliability of those hosted on the edge, while edge infrastructure handles those that do.

By pairing cloud and edge together, organizations can leverage the scalability and ease of deployment of cloud environments while still achieving high-performance goals that cloud data centers can’t always support due to network latency issues.

This isn’t to say that you always have to use cloud and edge at the same time. It’s entirely possible to host applications or data in the cloud without also running an edge environment. It’s also possible — although less common — to use edge without using a public cloud.

Cloud and edge supercharge performance
But in general, using cloud computing and edge computing at the same time can lead to the best overall outcome from a performance perspective. The only big downside is that it can be harder to manage a cloud environment and an edge environment at the same time, although platforms like Kubernetes simplify this by making it easy to deploy applications to both types of environments from a central management plane.

A global survey of 1,350 business and IT executives conducted by Jigsaw Research on behalf of NTT Ltd., a provider of IT services, suggests organizations are a lot more agile in the wake of the COVID-19 pandemic, but there’s still a significant gap in terms of prioritizing agility.

A full 89% of business and IT leaders agree COVID-19 has caused significant changes to their operating processes, with 87% also agreeing it has accelerated their digital transformation strategy. A third of respondents (33%) also noted they pivoted their technology focus to take advantage of a market opportunity or scale their operations.

However, well over a third of operational executives identified speed and agility as a core component of technology strategy, while only just over half of IT executives agreed (53%).

Overall, the survey found there is a consensus IT remains a key enabler of business efficiency. A total of 91.4% respondents agreed that enabling business efficiency is a key driver for their technology strategy, with 92% of IT respondents saying their overall technology strategy is aligned either fully or partially with the organization’s business strategy needs.

However, there’s a significant divide in terms of the importance of IT’s role in driving business efficiency. A technology strategy that drives business efficiency opportunities is seen as being most crucial by 70% of operations teams, yet only 48% of IT teams said the same.

Sanjay Lakhani, a principal consultant for NTT and head of the IT service management (ITSM) practice for the Americas region said that while the alignment between business operations and IT is not as close as it could be, a lot of improvement has been made. Prior to the COVID-19 pandemic, the divide between IT and the rest of the business was generally wider, noted Lakhani.

Now that more organizations are realizing how dependent they are on software, there’s a lot more focus on application development with business units, he added. There’s also more pressure on IT operations teams, which are being asked to keep pace with the rate at which digital business transformation initiatives have accelerated, added Lakhani.

Unfortunately, the survey also highlighted an emerging technology gap. The survey found 92% of business and IT leaders agreed emerging technology is crucial for their technology strategy, but only two-fifths (41%) believed they have the technology to meet their organization’s immediate objectives. Those result suggest a significant transition is underway, both in terms of how software is constructed and the type of IT infrastructure required.

In the meantime, it remains to be seen to what degree the COVID-19 pandemic has fundamentally altered the DNA of IT operations teams, but it’s apparent enterprise IT will never be the same. Most IT teams have embraced, to varying degrees, agile application development methodologies along with DevOps best practices. The challenge now is aligning the operational cadence IT teams follow with that of the rest of the business in the wake of the pandemic.

As the one-year anniversary of the pandemic declaration has come and gone, it is getting more difficult to remember what business was like before March 11, 2020. Although we miss the human interaction of office work; some things should be left in the past. Even though vaccinations have created a light at the end of the tunnel, what it illuminates will not be like the past. The top 7 tech trends for 2021 not only reflect the journey to the light but also what lies beyond it.

In short, the 7 tech trends are the way forward for business. In tandem, they are what you can use to thrive in a post-pandemic world. However, you can’t do this with an infrastructure that is decades old. In this series of blogs, we have been showing you how modernization in the cloud is the foundation for adopting the 7 trends. We have already tackled customer experience (CX), data integration, digital culture, digital innovation, the composable enterprise, and automation. This blog looks at the final trend, microservices and service mesh.

Angels in the architecture: Microservices
Microservices have been around since 2009 at least. Recently, as more tech giants have publicized their moves to microservices, they have really taken off. This is because they resolve a lot of the problems of monolithic application architectures, which develop performance issues when they are scaled and are hard to change quickly. So, what are microservices exactly?

Microservices.io has this definition: “an architectural style that structures an application as a collection of services.” Software functionality is isolated into multiple independent modules that are individually responsible for specific standalone tasks. The modules communicate with one another via simple, universally accessible application programming interfaces (APIs). As a result, one team’s changes won’t break an entire app, unlike in monolithic application architectures. Microservices can be deployed in virtual machines or containers; however, containers are becoming the microservices deployment method of choice.

Amazon is a microservices pioneer. It wanted a more robust application that could handle new features and functions quickly. So, it moved from a monolith to a fully distributed, decentralized services architecture. Another pioneer is Netflix, who has built one of the most well-known examples of a modern microservices architecture – one that features an API gateway that handles about two billion API edge requests a day, all supported by over 500 microservices.

Communication breakdown: Service mesh
As the Netflix example shows, in the microservices world, an application can consist of hundreds of services. Each service could have thousands of instances. If the container deployment method is being used, a container orchestrator like Kubernetes is constantly changing the instances. This creates incredibly complex service-to-service communication, which is essential to microservices during runtime.

To ensure end-to-end performance, reliability, and security, organizations are increasingly turning to a service mesh, which adds observability, security, and reliability features to applications by inserting them at the platform layer rather than the application layer. As a result, the application doesn’t need to implement these features, or even be aware of the service mesh.

Lyft is an example of company that uses a service mesh it developed itself. Lyft’s Envoy, one of the earliest service meshes, enables services to communicate with one another through a proxy that is outside the application. It supports L3/L4 filter architecture like a TCP/IP proxy, HTTP/2 based L7 filter architecture, service discovery and active/passive health checking, load balancing, authentication and authorization, and observability. Microsoft, HP, Walmart, and Nordstrom use another service mesh called Linkerd. Cash App, eBay, Giphy, and Intuit use Istio, Google’s and IBM’s joint mesh service.

Microservices, service mesh, and modern cloud infrastructures
Microservices, service mesh, containers, and Kubernetes all enable you to offer differentiated digital services to your customers or consumers. Gartner predicts that these technical advancements will be in place in 70% of organizations by 2022. They are a key aspect of digital transformation, and they can also help you make other tech trends, such as customer experience and automation, a reality.

What should not go unnoticed is these modern application structures were not built for the infrastructures of yesteryear. Although microservices and containers can run on virtual machines, they are more efficient, scalable, and powerful in the cloud. Plus, Kubernetes and service meshes need the cloud. Therefore, an on-premises data center that houses an aging mainframe, legacy applications, and a rigid database with high licensing costs is going to severely inhibit their use.

Instead, you should migrate your mainframe and legacy applications to a cloud infrastructure designed for microservices, service mesh, and more. To do this, you need a legacy modernization platform. With a legacy modernization platform, you move your applications to a cloud-based environment with no reformatting, code changes or user impact. Once in this environment, you can modernize your apps and by breaking them into faster, responsive, agile microservices, and set up your service mesh. To enable your service mesh to help your microservice applications share data with one another, you should also consider a modern RDBMS that runs in the cloud.

Ready to break free from monoliths?
Two TmaxSoft products, OpenFrame and Tibero, can provide the legacy modernization platform and RDBMS you need to make microservices and service mesh part of your digital transformation strategy. Both solutions make it easy to transition your application architectures to the cloud. Learn more about modernizing your legacy applications and moving away from on-premises architectures by reading our eBook on proven modernization strategies.

Kelly McClure
Kelly McClure is the Vice President of Global Marketing for TmaxSoft. Her 20-year marketing career spans both Fortune 1000 companies and fast growth technology startups. Kelly is responsible for leading TmaxSoft’s marketing strategy. She is experienced in aligning marketing and sales, building relevant content and messaging and developing integrated lead generation campaigns. Before joining TmaxSoft, Kelly served as the Vice President of Marketing for 10th Magnitude and held senior marketing roles with DataStax, BMC Software and Micro Focus. Kelly has a bachelor’s degree from Purdue University and an MBA from Loyola University Chicago.

Despite all the talk of cybersecurity capabilities being built in versus bolted on, security remains an afterthought for a vast majority of digital transformation activities such as mobility, cloud services and customer experience programs.
Too often, security is seen as slowing down a project rather than enabling its success, and with time pressure to get a project up and running, the lack of sound security considerations is a problem for organisations striving for true business resilience.

Ensuring that cyber risk management is part of your business resilience plan is not something that can be added after the event. It must be included from the outset and embedded across the organisation, from your technologies right through to your culture. Today, the pressure in the digital economy is to be ‘always on’, offering a continuously available service to customers. And that means that – more than protecting the enterprise – the challenge now is to ensure its resilience.

Red Hat is the world’s leading provider of enterprise open source solutions, using a community-powered approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies. They help you standardise across environments, develop cloud-native applications, and integrate, automate, secure, and manage complex environments with award-winning support, training, and consulting services.

Through Red Hat’s services, your business will be able to combine business continuity planning with risk management planning to better navigate disruptive change and protect shareholder value, this spills over into being strategic about cloud migration too.

As the world becomes more turbulent, organizations need to accelerate their business resilience planning.
As organisations continue to expand their digital footprint, the risks associated with digital business are increasing in quantity and complexity and the need to provide continuously available services to customers requires organizations to become more reliable and more resilient.

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

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

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

Publication of a new analytical documentation including clear synopsis of ongoing market developments governing Global MEA Cloud Infrastructure Services Market t has been accomplished, enabling rapid diversification of report repository. The report is mindfully designed to present a holistic overview of the market across historical and current timeframes, for the enablement of futuristic predictions and forecasts. The export includes a 360-degree outline of the market that consistently allows market participants to imbibe growth probabilities.

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The study encompasses profiles of major companies operating in the MEA Cloud Infrastructure Services Market. Key players profiled in the report include:
AWS
Microsoft Corporation
Google
IBM
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By the product type, the market is primarily split into
Public Cloud
Private Cloud
Hybrid Cloud

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SME
Large Organization

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North America (U.S., Canada, Mexico)
South America (Cuba, Brazil, Argentina, and many others.)
Europe (Germany, U.K., France, Italy, Russia, Spain, etc.)
Asia (China, India, Russia, and many other Asian nations.)
Pacific region (Indonesia, Japan, and many other Pacific nations.)
Middle East & Africa (Saudi Arabia, South Africa, and many others.)

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SEATTLE–(BUSINESS WIRE)–Today, Amazon Web Services (AWS), an Amazon.com company (NASDAQ: AMZN), announced that Siemens Smart Infrastructure, a business of the Siemens AG group focusing on energy distribution and intelligent buildings, is moving its SAP infrastructure to AWS. The business will migrate over 20 Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) systems, based on SAP HANA, to AWS before the end of 2021. These systems support business-critical processes in areas such as manufacturing, operations, and sales across different business units including Building Products, Electrical Products, Digital Grid, and Distribution Systems. Moving these workloads to AWS will enable Siemens Smart Infrastructure to significantly shorten hardware refresh cycles, increase agility to test and deploy new systems, and provide the foundation for the company’s future transformation using SAP S/4HANA technology. This is the first migration of production-scale SAP systems to the cloud across the Siemens AG group, with Siemens Smart Infrastructure forecasting significant cost savings over the course of the next three years.

Siemens Smart Infrastructure’s migration of SAP to AWS is a core part of the company’s strategic plan to accelerate the modernization of their business. Working alongside AWS as their preferred cloud provider for SAP, Siemens Smart Infrastructure will be able to integrate their SAP environments with a wide range of advanced AWS technologies, in areas such as analytics and machine learning, to uncover new business value and drive innovation. All of this was not previously possible for Siemens Smart Infrastructure, as their SAP systems were siloed off from the rest of their business in co-located data centers. As the foundation of their new cloud-based SAP environment, Siemens Smart Infrastructure will utilize a broad range of AWS’s Amazon Elastic Compute Cloud (Amazon EC2) instances, including memory-optimized R5 instances for real-time big data analytics workloads, as well as X1 and X1e instances built for high-performance databases, in-memory databases, and other memory-intensive enterprise applications.

Siemens Smart Infrastructure and AWS started their successful collaboration five years ago, when Siemens began moving core parts of its digital building technology to the cloud. The company is currently using a number of other advanced AWS services including Amazon Aurora (a MySQL and PostgreSQL-compatible relational database built for the cloud), AWS Lambda (AWS’s serverless compute service), and Amazon Neptune (a fully managed graph database service).

“2020 has been a year like no other, yet AWS has been a dedicated and trustworthy partner on our digital transformation journey,” said Sébastien Bey, CIO of Siemens Smart Infrastructure. “Our SAP environments are the backbone of our organization; by moving these foundational pieces of the Siemens Smart Infrastructure business to AWS, we are not only reducing costs and upgrading the service we provide to our internal businesses, but also fundamentally changing the way we use technology to transform our business, innovate, and deliver value.”

“AWS is thrilled to be working alongside a forward-thinking and customer-centric business like Siemens Smart Infrastructure as they connect buildings and cities with their advanced technologies. AWS delivers proven performance, insights, and agility to advance Siemens Smart Infrastructure’s mission of helping customers around the world run their infrastructure in the most efficient and sustainable way and ensure our long-term future on this planet,” said Carla Stratfold, Vice President of AWS Global Verticals and Strategic Accounts at Amazon Web Services, Inc. “Organizations around the world trust their most essential SAP workloads to AWS because of our unmatched track record, breadth and depth of services, and vibrant partner community that helps them to transform their SAP landscapes.”

Amazon Web Services

For 14 years, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud platform. AWS offers over 175 fully featured services for compute, storage, databases, networking, analytics, robotics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, virtual and augmented reality (VR and AR), media, and application development, deployment, and management from 77 Availability Zones (AZs) within 24 geographic regions, with announced plans for 15 more Availability Zones and five more AWS Regions in India, Indonesia, Japan, Spain, and Switzerland. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com.

Amazon

Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Fire tablets, Fire TV, Amazon Echo, and Alexa are just some of the products and services pioneered by Amazon. For more information visit www.amazon.com/about and follow @AmazonNews.

Siemens

Siemens Smart Infrastructure (SI) is shaping the market for intelligent, adaptive infrastructure for today and the future. It addresses the pressing challenges of urbanization and climate change by connecting energy systems, buildings and industries. SI provides customers with a comprehensive end-to-end portfolio from a single source – with products, systems, solutions and services from the point of power generation all the way to consumption. With an increasingly digitalized ecosystem, it helps customers thrive and communities progress while contributing toward protecting the planet. SI creates environments that care. Siemens Smart Infrastructure has its global headquarters in Zug, Switzerland, and has around 72,000 employees worldwide.

Sydney, Australia, June 17, 2020 – Trianz, a global digital transformation consulting and technology services firm, has attained the Amazon Web Services (AWS) DevOps Competency status.

This Competency has cemented Trianz’ position as a differentiated AWS Partner Network (APN) member that provides focused consulting, continuous integration & continuous delivery, monitoring, logging and performance management, and infrastructure as code services. The firm successfully fulfills the AWS Competency Program’s requirement of deep AWS expertise and seamless solution delivery.

With Trianz’ DevOps practice, clients can quickly build, test and deploy code for eliminating bugs, enhance mission functionality and implement cybersecurity protection efficiently, while also minimizing downtime and providing on-going 24x7x365 enterprise technical support. Trianz’ approach brings in greater visibility of most granular details on deployment and gives clients insights on the real progress.

“We are delighted that AWS recognizes the strength, depth and quality of our DevOps capabilities,” said Sameer Zaveri, Vice President – Cloud Practice at Trianz. “This validation underlines our proven expertise in helping customers transform their business by adopting Agile and DevOps practices. Our growth momentum will not stop as we continue to invest heavily in our DevOps capabilities and help clients in transforming their businesses.”

“We are thrilled to attain the APN DevOps Competency status,” said Eric Lucas, Director – Cloud Practice at Trianz. “By leveraging DevOps, our customers can accelerate speed to market through highly automated and efficient processes, which results in increased certainty and reduced risk. Also, our unique methodology for implementing DevOps use cases supported by the latest tools and technologies provides business agility to our customers, which is critical in this digital economy.”

To receive the designation, APN Partners must undergo rigorous technical validation related to DevOps, as well as an assessment of the security, performance, and reliability of their Amazon Web Services (AWS) solutions.

About Trianz
Trianz simplifies digital evolutions of companies from strategy through execution. Leveraging its global digital transformation database of over 1.5 million datapoints, Trianz provides digital maturity and benchmarking services, transformation strategy development and execution blueprint services. With a unique, multi-disciplinary and collaborative model, Trianz helps clients transition to new business models, digitalized processes and deliver great experiences utilizing Analytics, Digital, Cloud, Infrastructure and Cyber Security technologies.

Terminology and complexity can make any new concept scary. DevOps — a portmanteau of Development and Operations — can feel particularly intimidating because it inherits jargon and practices from two distinct disciplines. Three-letter acronyms, overlapping toolchains, and a steep-looking learning curve all contribute to that perception.

But the core goal of DevOps is straightforward: create consistency and repeatability for anyone on your team who needs to perform development or systems tasks. The underlying principle is that the people who write software and the people who keep that software running should collaborate closely — not hand off work over a wall.

Origins of DevOps: The Problem It Was Built to Solve

Before DevOps became a recognised discipline, the typical workflow looked like this: a software team pushed out code changes, and an operations team handled installation, configuration, and upgrades — often with little or no coordination. The result was a predictable set of problems: mismatched dependencies, conflicting OS patches, and subtle differences between production, staging, and test environments.

This “fire and forget” model created friction and blame culture. The developer’s infamous response — “works on my machine” — became a cliché precisely because environment mismatches were so common.

Just as Agile improved both the quality of software and the working conditions of teams producing it, DevOps aims to improve the quality of delivering that software. With a focus on transparency, automation, and shared ownership, it is good for engineers and good for the business they support.

DevOps Is Good for Developers

Automation

Developers instinctively look for ways to automate repetitive tasks in software. DevOps applies that same instinct to infrastructure. Clicking through cloud-provider consoles is a fine way to explore a new service, but it does not scale. Manual configuration is error-prone: it is easy to misconfigure a security rule, forget to shut down an idle resource, or apply settings inconsistently across environments. Codifying infrastructure removes that category of error entirely.

Scalable, Repeatable Configuration

Tools like Terraform give you granular, version-controlled, and repeatable control over cloud resources. Its declarative syntax makes it straightforward to see exactly how security groups, networking rules, and compute resources are configured at any point in time. Adding a resource is a few lines of code and a single command — not a multi-step journey through a web console.

Infrastructure as Living Documentation

Developers are rarely enthusiastic about writing documentation. Infrastructure as Code (IaC) sidesteps this problem: a well-structured set of Terraform or Ansible files is the documentation. A new team member can read those files and understand the network topology, security posture, and deployment model without needing a separate wiki page that may already be out of date. Diagrams and inline comments still add value, but the code itself captures the authoritative state of the system.

DevOps Is Good for Business

Resource Utilisation and Cost Control

A significant benefit of adopting DevOps practices is gaining clear visibility into what infrastructure is running and whether it is over- or under-utilised. When infrastructure is defined as code and subject to peer review, it becomes much harder to spin up servers unnecessarily or leave idle resources consuming budget. Developers and operations staff can collaborate on infrastructure changes using the same pull-request workflow they already use for application code.

Environment Repeatability

One of DevOps’s core tenets is that every environment — development, test, staging, and production — should be as identical as possible. When environments diverge, bugs that only appear in production become difficult and expensive to reproduce and fix. IaC tools make it practical to keep all environments in sync, dramatically reducing deployment-related defects.

Disaster Recovery

Repeatability has direct implications for disaster recovery. If your entire infrastructure is defined in code, rebuilding a lost system is a controlled, documented process rather than a chaotic scramble. A good DevOps workflow also brings development teams into disaster-recovery conversations early, allowing them to make application-level decisions — such as stateless service design or idempotent operations — that make recovery faster and more reliable.

Attracting and Retaining Engineers

Experienced engineers want to work in environments where they feel effective and empowered. A mature DevOps culture — with automated pipelines, peer-reviewed infrastructure changes, and shared ownership — signals to candidates that the organisation takes engineering craft seriously. That is a meaningful competitive advantage when hiring.

DevOps Is More Approachable Than It Looks

The breadth of the DevOps ecosystem — Terraform, Ansible, Kubernetes, Helm, Rancher, CI/CD platforms, observability stacks — can make the whole thing feel overwhelming. The good news is that you do not need all of it at once. Start with a specific pain point and solve it with a targeted tool. Each win builds confidence and skills for the next step.

Here are three practical entry points to begin a DevOps journey:

1. Manage Security Groups with Terraform

If you are struggling to keep track of AWS Security Group rules spread across multiple console screens, Terraform is an excellent first IaC project. Define your ingress and egress rules in .tf files, commit them to source control, and from that point on anyone can read the files to understand the current security posture. Adjusting an IP allowlist or opening a port becomes a code change subject to review, not an undocumented click in a web console. Terraform also supports importing existing resources, so you can adopt it incrementally without recreating infrastructure from scratch.

2. Consolidate Deployment Scripts with Ansible

If deployment involves a collection of ad-hoc shell scripts that are difficult to reason about or maintain, consider migrating them to Ansible. Ansible’s key property is idempotency: if a file or configuration is already in the desired state, Ansible skips the step rather than re-applying it. Its change-focused output makes it easy to see exactly what changed on each run — and equally importantly, what did not need to change. As your fleet of servers grows, Ansible makes it straightforward to apply the same configuration across all of them in a single command, and it integrates naturally with Terraform-provisioned instances.

3. Orchestrate Containers with Kubernetes

If your application has grown into a collection of Docker images — a microservices architecture — and managing their deployment, networking, and scaling is becoming unwieldy, Kubernetes is the industry-standard solution. It provides a declarative model for running and connecting containers, and every major cloud provider offers a managed Kubernetes service (EKS, GKE, AKS) that removes much of the operational overhead of running the control plane yourself.

Kubernetes does come with complexity: its configuration is expressed in YAML, and the ecosystem of supporting tools — Helm for package management, Rancher for multi-cluster management, among others — takes time to learn. It is not the right tool for every situation; if you are running a handful of containers, Docker Compose may be sufficient. But when you genuinely need to orchestrate many services at scale, Kubernetes is a powerful and well-supported choice.

DevOps Grows With Your Organisation

Modern DevOps tooling is designed to be adopted incrementally. You do not need a complete transformation on day one. Start with the pain point that costs your team the most time or causes the most incidents, apply a targeted solution, and iterate from there. As your product scales, your DevOps practice can scale with it — adding more automation, tighter feedback loops, and broader team involvement over time.

The complexity that makes DevOps look intimidating from the outside is, in practice, a collection of individually learnable tools and patterns. Each one exists to solve a real problem. Understand the problem first, then reach for the tool — and the learning curve becomes much less steep.

Cloud computing was one of the dominant themes in technology and business throughout 2019. The sector had been on a steep growth trajectory for several years, and 2019 was no exception. Synergy Research Group reported 37% year-over-year growth in the public cloud market overall, and noted that the public IaaS and PaaS markets had doubled in size in just two years, with forecasts pointing to another doubling within three years of that report.

Beyond raw market growth, a number of specific trends stood out as particularly significant in shaping how organizations approached cloud strategy, operations, and tooling in 2019. Here are the six that defined the year.

1. Rise of the Enterprise Cloud Manager

A growing proportion of technical leads at companies of all sizes began carrying job titles that explicitly referenced cloud platforms — for example, “Cloud Architect,” “AWS Engineer,” or “Enterprise Cloud Manager.” Analysis of user data from ParkMyCloud (a cloud cost management platform) found this trend reflected in its own customer base, indicating that cloud infrastructure management was maturing into a dedicated professional discipline.

This specialization signals that organizations are no longer treating cloud as a side responsibility of general infrastructure teams. Instead, dedicated roles now own the design, governance, and optimization of cloud environments — a significant shift in how IT departments are structured.

2. Consolidated Multi-Cloud Views

As enterprises expanded their use of multiple public cloud providers, the operational complexity of managing disparate environments grew proportionally. In 2019, there was a sharp increase in demand for consolidated dashboards and management tools that could provide a single, unified view across AWS, Azure, Google Cloud, and other providers.

Third-party tools addressing this need allow teams to orchestrate, govern, and optimize hybrid and multi-cloud environments from a single interface. A key driver cited by practitioners was operational simplicity: native cloud provider consoles, while powerful, are not always intuitive or well-suited to cross-cloud management tasks.

3. Multi-Cloud Becomes the Default Strategy

2019 was widely regarded as the year multi-cloud moved from a forward-looking strategy to mainstream practice. Studies conducted throughout the year suggested that more than 80% of enterprises had adopted or were actively pursuing a multi-cloud approach.

The top reasons organizations cited for going multi-cloud included:

• Avoiding vendor lock-in: Reducing dependency on any single cloud provider’s pricing, APIs, or availability.
• Best-of-breed services: Selecting the strongest offerings from each provider — for example, using Google Cloud for machine learning workloads while running core infrastructure on AWS.
• Application efficiency: Matching workloads to the cloud environment best suited to their performance and cost requirements.

4. Automation Expands Across Cloud Operations

Automation has long been a core principle of DevOps, and 2019 saw its continued and accelerating adoption within cloud environments. The ability to automate repetitive operational tasks — provisioning, scaling, patching, cost optimization, and compliance checks — remained one of the primary reasons organizations cited for adopting a DevOps culture alongside cloud infrastructure.

Tools supporting infrastructure-as-code (IaC) approaches, such as Terraform and AWS CloudFormation, continued to mature and gain wider adoption during this period, enabling teams to manage cloud resources programmatically rather than through manual console interactions.

5. Greater Abstraction: Serverless and PaaS Growth

A broader trend toward infrastructure abstraction continued throughout 2019. Developers increasingly adopted models that removed or minimized the need to think about the underlying infrastructure on which their applications run.

Serverless computing — where cloud providers automatically manage server provisioning, scaling, and availability — gained significant traction. Platform-as-a-Service (PaaS) offerings similarly allowed development teams to focus on writing and deploying code without managing virtual machines, operating systems, or network configuration. This shift contributed directly to faster development cycles and reduced operational overhead for engineering teams.

6. Containers Go Mainstream

Application containerization completed its transition from emerging technology to mainstream cloud practice in 2019. Docker-based container workflows became standard in many development pipelines, and Kubernetes emerged as the dominant container orchestration platform.

Managed Kubernetes services from major cloud providers — including Amazon EKS, Azure AKS, and Google GKE — saw growing adoption, lowering the barrier for organizations that wanted the benefits of container orchestration without the operational burden of managing the Kubernetes control plane themselves.

Containers offered engineering teams portability, consistency across development and production environments, and more efficient resource utilization — making them a natural fit for the multi-cloud strategies that were simultaneously becoming the norm.

Looking Ahead

The six trends described above — specialized cloud roles, consolidated multi-cloud management, multi-cloud adoption, automation, infrastructure abstraction, and container maturity — collectively illustrated that cloud computing in 2019 was not simply growing larger, but growing more sophisticated. Organizations were moving beyond basic cloud migration and beginning to optimize, govern, and architect cloud environments at a level of maturity the industry had not previously seen at scale.

These trends set the stage for the cloud landscape that followed in 2020 and beyond, as the discipline of cloud engineering continued to professionalize and the pace of infrastructure innovation showed no sign of slowing.

This article is written as an accessible, plain-language introduction to both technologies. It is not an exhaustive reference — think of it as the foundation you need before diving into the official documentation or a hands-on tutorial.

What Is Docker?

Docker is software that packages applications into portable, self-contained units called images. To appreciate why that matters, consider what it takes to share an application you have written.

Suppose you write a To-do app in Node.js. On your machine you have the right runtime installed, the right libraries, and the right environment variables set. You hand the source code to a friend who runs a different operating system. Immediately, problems appear: missing dependencies, wrong library versions, a different file-system layout. The code that works perfectly on your laptop does not run on theirs.

Docker solves this by bundling your application together with everything it needs to run — the OS filesystem, runtime, libraries, and configuration — into a single artefact called a Docker image. That image can be executed on any machine that has a Docker runtime installed, regardless of the underlying operating system.

Images vs. Containers

• Image: A read-only blueprint that describes what your application and its environment look like.
• Container: A running instance of an image — an isolated process on the host machine.

A common concern is that shipping an entire OS filesystem sounds heavy. In practice, minimal Linux distributions such as Alpine Linux are only a few megabytes, so images can remain very lean.

The workflow is straightforward:

1. Write a Dockerfile that describes how to build your image.
2. Run docker build to produce the image.
3. Push the image to a registry (such as Docker Hub or a private registry).
4. Anyone with Docker installed can pull and run the image with a single command.

So Where Does Kubernetes Come In?

Docker is excellent at packaging and running a single container. But real-world production services are rarely a single container running on a single machine. They involve many containers, many machines, traffic spikes, failures, rolling updates, and dozens of other operational concerns. Managing all of that by hand does not scale.

Returning to the To-do app example: you have packaged it as a Docker image and you want to expose it as a public web service. Now you face a new set of questions:

• What happens when the container crashes? Does it restart automatically?
• How do you handle a sudden surge in traffic without manually provisioning more servers?
• How do you roll out a new version without downtime?
• How do you route traffic to healthy instances only?
• How do you restrict which parts of your system can talk to each other?

Kubernetes answers all of these questions. It orchestrates your containers — deciding where they run, restarting them when they fail, scaling them up or down based on load, and exposing them to the network in a controlled way.

Core Kubernetes Concepts

You do not need to memorise every Kubernetes primitive before getting started, but the following four concepts form the foundation of almost everything else.

1. Clusters and Nodes

Kubernetes operates on a cluster — a group of machines (physical or virtual) that work together. Each machine in the cluster is called a node.

Nodes come in two roles:

• Control-plane nodes (formerly called master nodes): Run the Kubernetes system components that make cluster-wide decisions — what to run, where to run it, and whether the current state matches the desired state.
• Worker nodes: Run your application workloads. They receive instructions from the control plane and report back their status.

2. The Control Plane

The control plane is the brain of the cluster. Its core components include:

• kube-apiserver: The front door to the cluster. Every interaction — from the CLI, from internal components, from external tooling — goes through the API server.
• kube-scheduler: Watches for new workloads and decides which node they should run on, based on available resources and constraints.
• kube-controller-manager: Runs control loops that continuously reconcile the current state of the cluster with the desired state you have declared.

Worker nodes run two key agents:

• kubelet: Ensures containers described in Pod specs are running and healthy on that node.
• kube-proxy: Manages network rules on the node to enable service-to-service communication within the cluster.

3. Declarative Configuration

You interact with Kubernetes by describing what you want, not how to achieve it. You write a configuration file — in YAML or JSON — that declares the desired state, and Kubernetes works continuously to make reality match that declaration.

A minimal example of a Kubernetes Deployment manifest:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: todo-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: todo
  template:
    metadata:
      labels:
        app: todo
    spec:
      containers:
        - name: todo
          image: your-registry/todo-app:latest
          ports:
            - containerPort: 3000

This tells Kubernetes: run three copies of this container image and keep them running. If one crashes, Kubernetes automatically starts a replacement.

4. kubectl — The CLI Tool

The primary way to interact with a Kubernetes cluster is through kubectl, the official command-line interface. Common operations include:

• kubectl apply -f deployment.yaml — apply a configuration file to the cluster.
• kubectl get pods — list running pods.
• kubectl logs <pod-name> — stream logs from a container.
• kubectl describe pod <pod-name> — inspect a pod’s events and status.

What Kubernetes Handles for You

To make the value concrete, here is a summary of what Kubernetes manages once your application is deployed:

• Self-healing: Failed containers are restarted automatically. Nodes that become unresponsive cause their workloads to be rescheduled elsewhere.
• Horizontal scaling: Increase or decrease the number of running replicas with a single command or automatically via a Horizontal Pod Autoscaler.
• Rolling updates and rollbacks: Deploy a new version gradually, with automatic rollback if health checks fail.
• Service discovery and load balancing: Kubernetes assigns DNS names to services and distributes traffic across healthy pods.
• Secret and configuration management: Sensitive data (API keys, passwords) and configuration values can be injected into containers without baking them into the image.
• Storage orchestration: Automatically mount storage systems — local disks, cloud volumes, network file systems — to containers that need them.

Getting Started: Practical Advice

Before standing up a full production cluster, use local tooling to learn the concepts without cloud costs:

• minikube — runs a single-node Kubernetes cluster inside a VM or container on your laptop. Ideal for development and experimentation.
• kind (Kubernetes IN Docker) — runs Kubernetes clusters using Docker containers as nodes. Fast to start and popular for CI pipelines.
• k3s — a lightweight, production-grade Kubernetes distribution suited to edge, IoT, and resource-constrained environments.

For production deployments on AWS, tools such as kops (Kubernetes Operations) and managed services such as Amazon EKS, Google GKE, or Azure AKS significantly reduce the operational overhead of managing the control plane yourself.

Further Reading

The official Kubernetes documentation is thorough and well-maintained. The interactive tutorial at kubernetes.io/docs/tutorials/kubernetes-basics walks you through deploying, scaling, and updating an application against a live cluster — all from the browser, no local setup required.

Once you are comfortable with the basics, explore the concepts reference at kubernetes.io/docs/concepts to understand the full object model and how the scheduler, controller manager, and kubelet interact.

Kubernetes has a steep-looking learning curve from the outside, but once the mental model clicks — declare what you want, let Kubernetes figure out how — the pieces fall into place quickly.

Gartner’s 2019 IaaS Magic Quadrant: The Big Picture

Research and advisory firm Gartner published its 2019 Magic Quadrant for Cloud Infrastructure as a Service (IaaS) Worldwide, placing Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform in the Leaders quadrant. Of the three, AWS retained the top position, distinguished by the breadth, maturity, and continued innovation of its service portfolio. Microsoft Azure ranked second — ahead of Google Cloud — but notably lost ground on the “Ability to Execute” axis compared to the previous year’s report.

The report offers a structured view of where each major cloud provider stands in terms of both current delivery capability and long-term strategic vision. For teams evaluating cloud platforms or benchmarking vendor commitments, understanding where analysts see gaps is as important as understanding where providers excel.

Microsoft Azure: Strengths Identified by Gartner

• Deep Microsoft ecosystem integration: Enterprises already committed to Microsoft technology tend to choose Azure as their primary IaaS and PaaS provider. The end-to-end experience for building .NET applications in Visual Studio and deploying them directly to Azure was cited as particularly strong. Microsoft’s ability to co-sell Azure alongside its broader product portfolio (Office 365, Dynamics, etc.) is a significant adoption driver.
• Edge computing and IoT: Azure offers a well-integrated approach to edge and Internet of Things workloads, spanning from hyperscale data centers through edge solutions such as Azure Stack and Data Box Edge. This coherent edge-to-cloud story is a differentiator for industrial and distributed-computing use cases.
• Openness and innovation: By 2019, approximately 50% of Azure workloads were reported to be Linux-based, reflecting a meaningful shift toward open-source compatibility. Gartner also highlighted Microsoft’s strategy of bringing technology partners — including VMware, NetApp, Red Hat, Cray, and Databricks — into Azure as native, first-party offerings.

Microsoft Azure: Cautions Identified by Gartner

• Reliability and outages: Gartner flagged recurring reliability issues as a meaningful challenge. Since September 2018, Azure experienced multiple service-impacting incidents, including significant outages affecting Azure Active Directory. A key concern was that many of these incidents left customers with limited or no controls to mitigate the resulting downtime.
• Implementation expectations vs. reality: Gartner clients reported difficulties delivering projects on time and within budget. The report attributed this partly to Microsoft setting unrealistically high expectations during the sales cycle, combined with field sales teams that were not always well-positioned to scope Azure engagements accurately.
• Technical support quality and cost: Enterprises frequently criticized the quality of Microsoft’s technical support and field solution architects, alongside rising support costs. Gartner noted that poor support experiences negatively affect customer satisfaction and slow Azure adoption and spending growth.

Azure Revenue Growth: Strong Numbers, but a Long Road to Close the Gap

Microsoft’s FY19 Q4 earnings results (reported around the same time as this analysis) showed Azure revenue growing 64% year-over-year — a strong headline figure by any measure. However, AWS retains a substantially larger absolute market share, meaning that a 64% growth rate on a smaller base does not automatically close the gap in dollar terms. Analysts at the time suggested that to meaningfully challenge AWS’s lead within a few years, Azure would need to sustain triple-digit percentage growth rates — a bar that becomes progressively harder to clear as the business scales.

What This Means for Engineering and Platform Teams

The Gartner findings carry practical implications for DevOps and platform engineering teams evaluating or already operating on Azure:

• Design for resilience: Given the documented reliability incidents — particularly around identity services — teams should architect for Azure Active Directory failures using retries, circuit breakers, and multi-region redundancy where business requirements justify the cost.
• Validate sales commitments in writing: The caution around unrealistic implementation expectations is a known pattern with large cloud vendors. Require detailed Statements of Work, reference architectures, and explicit SLA definitions before committing to timelines or budgets.
• Budget for support tiers: If Azure is a primary platform, factor in the cost of elevated support plans early. Relying on community or basic support for production workloads may create risk given the quality concerns Gartner cited.
• Leverage the Microsoft ecosystem where it adds value: If your organization runs .NET, Active Directory, or Microsoft 365 heavily, Azure’s native integrations are a genuine productivity advantage — lean into them rather than defaulting to lowest-common-denominator cloud patterns.

Note: This article reflects the Gartner Magic Quadrant findings as reported in mid-2019. Cloud provider capabilities, market positions, and pricing change rapidly. Consult the most recent Gartner publications and vendor documentation before making current infrastructure decisions.

The Traditional Shared Services Model

For roughly two decades, labor arbitrage and shared services have operated in close alignment. Shared service organizations are built around functional expertise stacked in layers: infrastructure, security, application development and maintenance, compliance, and so on. Each layer has dedicated champions responsible for delivering that function cost-effectively and at a defined quality level.

Labor arbitrage fits naturally into this structure. Because the work is organized by function rather than by business outcome, tasks within each layer can frequently be performed more cost-effectively by offshore teams. The model rewards low unit costs per function and scales by adding people within those functional layers.

How DevOps Restructures the Stack

DevOps introduces a fundamentally different organizational construct. Instead of aligning work by functional expertise, DevOps aligns work by business service. Cross-functional teams own the entire delivery stack — development, testing, infrastructure, and operations — for a specific business capability.

A useful illustration is the treatment of infrastructure, application development, and testing. In a traditional shared services model, each of these is a separate functional silo with its own management chain. In a DevOps model, these workloads move into a software-defined environment where a single cross-functional team holds end-to-end responsibility. The functional stack effectively collapses into a unified, product-oriented team.

Why DevOps Is Disruptive to Labor Arbitrage

This restructuring creates three specific pressures on the shared services and labor arbitrage model:

1. Fewer people are needed. Automating infrastructure through software-defined environments and shifting operational responsibilities to the development team reduces the total headcount required. When you need fewer people overall, the cost advantage of offshore labor becomes proportionally less significant.
2. Teams gravitate toward the business. Because DevOps teams are aligned by business service, they require tight, continuous communication with business stakeholders. That communication need creates a natural pull toward co-location — same time zone, same building, or at minimum close proximity. The distributed, time-shifted model that underpins labor arbitrage works against this communication requirement. The “follow the sun” productivity argument, which relies on handing off work across time zones, does not translate well to DevOps, where synchronous collaboration is a core productivity driver.
3. The success metric changes. Shared service organizations optimize for low unit cost per function. DevOps teams optimize for high business impact delivered at speed. These are different objective functions, and they reward different organizational designs. As companies commit to genuine DevOps operating models, the metrics that justified labor arbitrage become less relevant.

Offshore DevOps: A Different Pattern

Some large technology organizations — Microsoft among them — do operate DevOps teams in offshore locations. However, the model they use differs materially from classical labor arbitrage. Rather than distributing individual functional specialists across locations, they relocate entire cross-functional teams offshore. The team remains co-located and autonomous; only its geography changes. This preserves the communication and collaboration properties that make DevOps effective, while still achieving geographic cost advantages.

This is a meaningful distinction. Moving a whole product team to a lower-cost location is not the same as disaggregating functional work and distributing it across offshore specialists. The former is compatible with DevOps principles; the latter largely is not.

What This Means for IT Service Delivery

Organizations evaluating or expanding their DevOps adoption should consider the following implications:

• Sourcing strategies need revisiting. Contracts and governance structures designed around functional shared services may not map cleanly onto DevOps team topologies. Re-examine sourcing assumptions as DevOps matures in the organization.
• Co-location or team-level offshoring should be the unit of analysis. If geographic cost savings are still a business requirement, evaluate moving complete product teams rather than individual functional roles.
• Measure business outcomes, not unit costs. Continuing to measure DevOps teams using shared-services metrics (cost per ticket, cost per function) will produce misleading signals. Adopt outcome-focused metrics: deployment frequency, lead time for changes, mean time to restore, and change failure rate.
• Automation investment reduces the labor base. Infrastructure-as-code, CI/CD pipelines, and test automation reduce repetitive manual work — the category of work most amenable to labor arbitrage. The more mature the automation, the smaller the pool of arbitrageable tasks.

Summary

DevOps does not simply speed up existing IT delivery processes — it restructures who does the work, how they are organized, and what they are optimizing for. The functional stack of the shared services model gives way to cross-functional teams aligned to business outcomes. As a consequence, the labor arbitrage equation that sustained shared services for two decades becomes progressively less applicable. Organizations that recognize this structural shift early will be better positioned to redesign their sourcing and governance models before legacy contracts and org structures become blockers to DevOps maturity.

Introduction
Docker Datacenter (DDC) is the enterprise container platform from Docker to be used across the entire software supply chain. It is a fully-integrated solution for container-based application development, deployment, and management. With integrated end-to-end security, DDC enables application portability by abstracting your infrastructure so that applications can move seamlessly from development to production.

What You Will Learn
This reference architecture describes a standard, production-grade, Docker Datacenter deployment. It also details the components of DDC, how they work, how to automate deployments, how to manage users and teams, how to provide high availability for the platform, and how to manage the infrastructure.

Some environment-specific configuration details are not be provided. For instance, load balancers vary greatly between cloud platforms and on-premises infrastructure platform. For these types of components, general guidelines to environment-specific resources are provided.

Understanding Docker Datacenter Components
From development to production, Docker Datacenter provides a seamless platform for containerized applications both on-premises and on the Cloud. It includes three components:

• Docker CS Engine, the commercially supported Docker Engine
• Docker Trusted Registry (DTR), a resilient and secure image management repository
• Universal Control Plane (UCP), the web-based, unified cluster and application management solution

Together they provide an integrated solution with the following design goals:

• Agility — the Docker API is used to interface with the platform so that operational features do not slow down application delivery
• Portability — the platform abstracts details of the infrastructure for applications
• Control — the environment is secure by default, provides robust access control, and logging of all operations

To achieve these goals the platform must be resilient and highly available. This reference architecture demonstrates this robust configuration.

Docker CS Engine
The building block of Docker Datacenter, the Docker Commercially Supported Engine (CS Engine) is responsible for container-level operations, interaction with the OS, providing the Docker API, and running the Swarm cluster. The Engine is also the integration point for infrastructure, including the OS resources, networking, and storage.

Universal Control Plane
UCP extends CS Engine by providing an integrated application management platform. It is both the main interaction point for users and the integration point for applications. UCP runs an agent on all nodes in the cluster to monitor them and a set of services on the controller nodes. This includes identity services to manage users, Certificate Authorities (CA) for user and cluster PKI, the main controller providing the Web UI and API, data stores for UCP state, and a Classic Swarm service for backward compatibility.

Docker Trusted Registry
The DTR is an application managed by, and integrated with UCP, that provides Docker images distribution and security services. The DTR uses UCP’s identity services to provide Single Sign-On (SSO), and establish a mutual trust to integrate with its PKI. It runs as a set of services on one or several replicas: the registry to store and distribute images, an image signing service, a Web UI, an API, and data stores for image metadata and DTR state.

Swarm Mode
To provide a seamless cluster based on a number of nodes, DDC relies on CS Engine’s Swarm-mode capability. Swarm-mode divides nodes between workers, nodes running application workloads defined as services, and managers, nodes in charge of maintaining desired state, managing the cluster’s internal PKI, and providing an API. Managers can also run workloads, in a DDC environment they run UCP controllers, and shouldn’t run anything else.

The Swarm-mode service model provides a declarative desired state for workloads, scalable to a number of tasks (the service’s containers), accessible through a stable resolvable name, and optionally exposing an end-point. Exposed services are accessible from any node on a cluster-wide reserved port, reaching tasks through the routing mesh, a fast routing layer using high-performance switching in the Linux Kernel. This set of features enable internal and external discoverability for services, UCP’s HTTP Routing Mesh (HRM) adding hostname-to-service mapping.

A Standard Deployment Architecture
This section demonstrates a standard, production-level architecture for Docker Datacenter using 10 nodes: 3 UCP controllers, 3 nodes for the DTR, and 4 worker nodes for application workloads. The number of worker nodes is arbitrary, most environments will have more depending on applications needs, it doesn’t change the architecture or the cluster configuration.

Access to the environment is done through 3 Load Balancers (or 3 load balancer virtual hosts) with corresponding DNS entries for the UCP controllers, the DTR replicas, and the applications running in the cluster.

The DTR replicas use shared storage for images. S3-compatible object storage (the default) and NFS storage are both covered in this section

Node Size
A node is a machine in the cluster (virtual or physical) with Docker Engine running on it. When provisioning each node, assign it a role: UCP Controller, DTR, or worker node so that they are protected from running application workloads.

To decide what size the node should be in terms of CPU, RAM, and storage resources, consider the following:

1. All nodes should at least fulfil the minimal requirements, for UCP 2.0 2GB of RAM and 3GB of storage. More detailed requirements are in the UCP documentation.
2. UCP Controller nodes should be provided with more than the minimal requirements, but won’t need much more if nothing else runs on them.
3. Ideal Worker nodes size will vary based on your workloads, it is impossible to define a universal standard size.
4. Other considerations like target density (average number of container per node), wether one standard node type or several are preferred, and other operational considerations might also influence sizing.

If possible, node size should be determined by experimentation and testing actual workloads, and they should be refined iteratively. A good starting point is to select a standard or default machine type in your environment and use this size only. If your standard machine type provides more resources than the UCP Controllers need, it makes sense to have a smaller node size for these. Whatever the starting choice, it’s important to monitoring resource usage and cost to improve the model.

Two example scenarios:

• One standard node size: 2VCPU, 8GB RAM, 20GB storage
• Two node sizes: 2VCPU, 8GB RAM, 20GB storage for UCP Controllers and 4VCPU, 64GB RAM, 40GB storage for worker nodes

Depending on your OS of choice, storage configuration for Docker Engine might require some planning. Refer to the Support Matrix to see what storage drivers are supported for your host OS. This is especially important if you are using RHEL or CentOS, which use device mapper with direct-lvm.

Load Balancers

Load balancers configuration should be done before installation, including the creation of DNS entries. Most load balancers should work with DDC. The only requirements are TCP passthrough and the ability to do healthchecks on an HTTPS endpoint.

In our example architecture, UCP controllers ensure UCP resiliency in case of node failure or controller reconfiguration. Access to UCP through the GUI or API is always done using TLS. The load balancer is configured for simple TCP pass-through on port 443, using a custom HTTPS health check at https://<ucp_controller>/_ping.

Be sure to create a DNS entry for the UCP host such as ucp.example.com and point to the load balancer.

The setup for the three DTR replicas is similar to setting up the UCP controllers. Again, use TCP pass-through to port 443 on the nodes, except the health check is at https://<dtr_replica_node>/health.

Create a DNS entry for the DTR host such as dtr.example.com and point it to the load balancer. It’s important to keep it as concise as possible because it will be part of the full name of images. For example, user_a‘s webserver image will be named dtr.example.com/user_a/webserver.

The application load balancer provides access to services HTTP endpoints exposed through UCP’s HTTP Routing Mesh (HRM). HRM provides a reverse-proxy to map domain names to services that expose ports and are attached to the ucp-hrm overlay network. As an example, the voting application exposes the vote service’s port 80. The HRM maps http://vote.apps.example.com to this port on the ucp-hrm overlay network, and the application LB itself maps *.apps.example.com to nodes in the cluster.

DTR Storage
The DTR usually needs to store a large number of images. It uses external storage (S3, NFS, etc.), not node storage so that it can be shared between DTR replicas. The DTR replicates metadata and configuration information between replicas, but not image layers themselves. To determine storage size, start with the size of the existing images used in the environment and evolve from there.

It’s best to use an existing storage solution in your environment so that image storage can benefit from existing operations experience. If you have to chose a new solution, consider using S3-compatible object storage, which maps more closely to registry operations.

Recommendations for the Docker Datacenter Installation
This section will detail the installation process for the architecture and provide a checklist. It is not a substitute for the documentation, which provides more details and is authoritative in any case. The goal is to help you define a repeatable (and ideally automated) process to deploy, configure, upgrade and expand your DDC environment.

The three main stages of a DDC installation are:

1. Deploying and configuring the infrastructure (hosts, network, storage).
2. Installing and configuring the Docker Engine, running as an application on the hosts.
3. Installing and configuring UCP and the DTR, delivered as containers running on the engine.

Infrastructure Considerations
The installation documentation details infrastructure requirements for DDC. It is recommended to use existing or platform specific tools in your environment to provide standardized and repeatable configuration for infrastructure components.

docker run --rm -v /var/run/docker.sock:/var/run/docker.sock \
  -v /tmp/docker_subscription.lic:/config/docker_subscription.lic \
  -e UCP_ADMIN_PASSWORD=password --name ucp docker/ucp install --host-address IP_or_interface \
  --san manager1.example.com --san ucp.example.com 

docker swarm join-token worker

docker swarm join \
--token SWMTKN-1-00gqkzjo07dxcxb53qs4brml51vm6ca2e8fjnd6dds8lyn9ng1-092vhgjxz3jixvjf081sdge3p \
192.168.65.2:2377

UCPID=$(docker run --rm -i --name ucp -v /var/run/docker.sock:/var/run/docker.sock docker/ucp id)
docker run --rm -i --name ucp -v /var/run/docker.sock:/var/run/docker.sock docker/ucp backup --id $UCPID --passphrase "secret" > /tmp/backup.tar

docker logs ucp-controller

docker service create --name www -p 80:80 --label com.docker.ucp.access.label=development nginx

curl --silent --insecure --header "Authorization: Bearer $(curl --silent --insecure \
     --data '{"username":"<admin-username>","password":"<admin-password>"}' \
    https://<UCP-domain-name>/auth/login | jq --raw-output .auth_token)" \
    https://<UCP-domain-name>/enzi/v0/accounts | jq .

docker exec -it ucp-auth-api sh

enzi db-status

The Original Promise of Hybrid Cloud

For several years, hybrid cloud generated significant enthusiasm in enterprise IT circles. The concept was straightforward: run mission-critical, sensitive applications on-premises where security and control were tightest, then “burst” into a public cloud provider when additional capacity was needed. On paper, this offered the governance of private infrastructure with the elasticity of a commodity cloud service.

In practice, widespread adoption ran into serious obstacles. Two problems dominated: network latency between on-premises and cloud stacks degraded application performance, and the operational complexity of orchestrating heterogeneous platforms — different APIs, toolchains, networking models, and security policies — proved far harder to manage than vendors had suggested.

Does a DevOps and Container-First World Change Anything?

As container runtimes and orchestration platforms (such as Kubernetes) became mainstream, the abstraction layer between environments improved substantially. Containers package an application and its dependencies together, making it theoretically easier to run the same workload on-premises or in a public cloud without re-architecting it. This directly addresses one of the classic hybrid pain points: environment inconsistency.

Similarly, a mature DevOps culture — with infrastructure-as-code, CI/CD pipelines, and unified observability tooling — reduces some of the operational friction that plagued early hybrid deployments. So the technical barriers have genuinely lowered. Yet the author of the original piece, and many practitioners since, argue that these improvements do not make hybrid a destination; they simply make the transition to full cloud adoption less painful.

Five Reasons Hybrid Cloud’s Days May Be Numbered

1. Hybrid Was Partly a Vendor-Driven Narrative

Critics argue that the loudest early advocates of hybrid cloud were hardware manufacturers, network equipment vendors, and storage companies — organisations whose revenue depended on enterprises continuing to purchase and refresh physical infrastructure. The hybrid framing allowed them to position on-premises kit as a complement to cloud rather than a liability to be retired. As the ROI of running your own hardware estate has eroded, the vendor motivation behind some of that messaging has become clearer.

2. Hybrid Adds Complexity, Not Simplicity

One of the foundational benefits organisations seek from cloud adoption is operational simplicity: fewer data centres to manage, standardised APIs, and commodity billing that aligns cost with actual consumption. Hybrid environments undercut this promise. Teams must:

• Test applications across multiple environments with potentially different toolchains.
• Maintain separate cost models and billing processes for on-premises and cloud spend.
• Build and retain staff skilled across both worlds, which increases hiring and training costs.
• Manage inter-environment networking, security policies, and identity federation simultaneously.

Each of these adds overhead. For most organisations, this overhead is only justifiable as a temporary state.

3. Hybrid Is a Migration Phase, Not a Long-Term Strategy

Many enterprises running hybrid environments today are not doing so by choice as a permanent architecture — they are mid-migration. Legacy systems that cannot be refactored immediately remain on-premises while newer workloads move to the cloud. The on-premises footprint is being “sweated” until its useful life expires or until re-platforming becomes feasible. Calling this hybrid is technically accurate, but it is a transitional state rather than a strategic destination.

4. Cloud Security Has Matured Significantly

In the early days of public cloud adoption, security was the most commonly cited reason for keeping workloads on-premises. That argument has become harder to sustain. Major cloud providers invest heavily in compliance certifications, encryption at rest and in transit, identity and access management tooling, and dedicated security services. Highly regulated sectors — financial services, healthcare, government — have increasingly moved sensitive workloads to certified cloud environments. The security argument for mandatory on-premises hosting applies to a narrower set of use cases than it once did.

5. The Risk of Standing Still Now Outweighs the Risk of Moving

For many years, technology leaders could reasonably argue that the risk of a large, complex cloud migration outweighed the risk of deferring it. That calculus has shifted. Businesses face competitive pressure to iterate faster, and business units that cannot get infrastructure provisioned quickly enough will find ways to acquire it themselves — creating ungoverned “shadow IT” that is harder to secure and audit. IT departments that do not lead the cloud journey risk losing influence over it entirely.

What About Vendor Hybrid Offerings?

At the time the original article was written (early 2017), offerings such as Azure Stack and VMware Cloud on AWS were either not yet available or in very early stages. The author was sceptical that they would fundamentally change the calculus, arguing that owning hardware remains an outdated capital investment: unlike real property, IT infrastructure depreciates rapidly and does not benefit from the economies of scale that a hyperscaler can achieve.

Since 2017, those products have shipped and matured, and a genuine market has developed around them — particularly for edge computing, data sovereignty requirements, and latency-sensitive workloads. This is one area where the original argument merits updating: there are legitimate architectural reasons to run cloud-managed infrastructure on-premises in specific contexts. However, these remain specialised scenarios, not a general-purpose argument for maintaining a large private hardware estate.

The Bigger Picture: Cloud as Business Modernisation

The organisations making the most decisive moves to cloud are not primarily driven by cost savings alone, though those are real. The deeper motivation is business agility: the ability to scale capacity on demand, adopt new managed services without infrastructure procurement cycles, and respond to market disruption faster than competitors locked into multi-year hardware refresh cycles.

When you consider the engineering investment the largest cloud providers make in their infrastructure, networking, and managed service portfolios, very few organisations can replicate that capability internally at comparable cost and quality. Infrastructure is rarely a core business differentiator — and treating it as a commodity, procured and consumed as needed, frees engineering teams to focus on what actually differentiates the product.

Key Takeaways

• Hybrid cloud was a genuine engineering concept that addressed real concerns about latency, security, and control — but execution proved difficult at scale.
• Containers and DevOps tooling have reduced environment inconsistency, making hybrid architectures easier to operate technically, but this does not make hybrid a permanent destination for most organisations.
• For most enterprises, hybrid represents a transitional phase during migration to a cloud-first or cloud-only model, not a long-term infrastructure strategy.
• Genuine hybrid use cases exist — edge computing, data residency compliance, ultra-low latency requirements — but they are narrower than the broad hybrid narrative once suggested.
• The competitive and governance risks of delaying cloud adoption now often outweigh the risks of executing a migration project.

Why “Going to 11” Is the Wrong Goal — Unless You Do It Right

“These go to 11.” The immortal line from the 1984 mockumentary This Is Spinal Tap, where fictional guitarist Nigel Tufnel proudly reveals that his amplifier’s volume dial goes one notch beyond the standard maximum. It’s absurd — and that’s exactly the point.

Applied to DevOps, the metaphor holds up surprisingly well. Continuous delivery pipelines make it technically possible to crank deployment frequency to seemingly absurd levels: hundreds of releases per day, every code commit pushed straight to production. But raw deployment volume — like a volume dial cranked to 11 — is meaningless if the signal is distorted. Speed without quality, resilience, or compliance is just noise.

Mature DevOps practitioners understand this intuitively. The real goal is to push every dimension of software delivery to its maximum simultaneously: deployment frequency, application quality, test coverage, observability, compliance, and supportability. That requires more than a fast pipeline — it requires deeply integrated toolchains that make quality a built-in property of every release, not an afterthought.

11 Toolchain Integrations That Amplify DevOps Across Every Dimension

The following integrations address the cross-functional collaboration and automation that separate high-performing DevOps teams from those that simply deploy frequently. Consider them a starting checklist, not an exhaustive list.

1. Auto-Generate Test Cases From Agile User Stories

Connect your agile planning tool to your test management platform so that user stories automatically produce the minimum viable set of test cases covering 100% of the described functionality. Each test case should link to the correct test data and expected outcomes, reducing manual translation effort and the risk of untested acceptance criteria slipping into production.

2. Real-Time Release Content Dashboards

Give every stakeholder — dev, QA, ops, and business — a unified, live view of what is in each release: which user stories, features, bug fixes, and dependencies are moving through the pipeline and at what stage. A shared dashboard reduces the coordination overhead that typically kills release velocity and helps teams reconcile cross-application dependencies before they become production incidents.

3. Environment-Aware Test Data Attachment

Automatically attach test data criteria to test cases at the point of creation in your agile planning tool. As a feature is promoted from development to test to pre-production, the test execution engine should automatically resolve and use data that already exists in the target environment — eliminating the classic “works on my machine” failure mode caused by mismatched data sets.

4. APM-Gated Continuous Integration Builds

Integrate Application Performance Management (APM) directly into your CI system so that every build is evaluated against defined pass/fail performance thresholds — not just functional test results. Developers should be able to trigger this check from within the tools they already use (such as the Jenkins dashboard) and be linked directly to the relevant APM data when a build fails, shortening the feedback loop from hours to minutes.

5. Service Virtualization With Integrated Test Data Management

Use service virtualization to simulate downstream services and APIs that are unavailable, unstable, or cost-prohibitive to use in test environments. Combine this with a test data management layer that injects referentially correct, compliant data into those virtual services. This combination dramatically increases test environment availability and fidelity while reducing the compliance risk associated with using real production data in non-production environments.

6. Automated Test Data Generation and Reservation in Deployment Workflows

Embed test data provisioning directly into your deployment pipeline. Rather than requiring testers to manually request and configure data before a test run, each deployment workflow should automatically generate, reserve, and assign the necessary test data sets. This removes a common bottleneck that inflates cycle time between code completion and validated release.

7. Automated Test Execution Tied to Go/No-Go Promotion Gates

Automatically trigger the appropriate test suites when a build reaches a new pipeline stage and feed the results back into the release management system. Define promotion rules — pass rates, severity thresholds, coverage minimums — so that promotion to the next environment happens automatically when criteria are met and is blocked when they are not. This removes human latency from routine promotion decisions without sacrificing quality control.

8. Virtual Environment Provisioning Within Deployment Workflows

Provision virtual services and spin up complete virtual test environments as a native step in the deployment pipeline, rather than requiring separate infrastructure requests. Teams no longer need to wait for physical or cloud environments to be built, configured, and stabilised before testing can begin. This is particularly impactful for integration and system testing stages, which traditionally carry the longest environment lead times.

9. APM Deployment as Part of the Release Workflow

Treat APM instrumentation and configuration as a first-class deployment artifact. When an application is deployed, APM agents and dashboards should be automatically configured alongside it, with baseline performance metrics captured immediately before and after the release event. This gives operations teams a clean before/after comparison for every deployment rather than discovering regressions during incidents.

10. API Management Integrated With APM for Business Transaction Tracing

Combine API gateway telemetry with APM data to gain end-to-end visibility into how specific APIs affect application performance and customer experience. The integration should support proactive alerting on emerging performance degradation and the ability to trace the full interaction chain of APIs involved in a specific business transaction — not just individual service health checks in isolation.

11. Load Test Scenarios Embedded in APM Business Analysis

Allow APM users to incorporate load test results — including throughput, response time distributions, and error rates under synthetic load — directly into their performance dashboards and business analysis workflows. This connects synthetic performance data with real-world APM metrics, enabling earlier detection of capacity and scalability issues before they manifest under production traffic.

The Underlying Principle: Data, Metrics, and Context Flow Across the Whole Pipeline

What these 11 integrations share is a common architectural principle: the right data and context must be available to the right person in the right tool at the right moment in the pipeline. A test result is only actionable if a developer can see it in their CI dashboard. An APM alert is only useful if it’s tied to a specific deployment event. A go/no-go gate only works if the criteria are grounded in business requirements, not just technical thresholds.

When toolchains are integrated with this level of intentionality, no individual discipline — development, testing, or operations — becomes a bottleneck. Each function operates with full context, and the pipeline as a whole moves with purpose rather than lurching from handoff to handoff.

Start With the Integrations That Address Your Biggest Constraint

Not every team needs all 11 integrations immediately, and implementing them all at once without a clear value hypothesis is a recipe for a slow, expensive toolchain project that never ships. A more pragmatic approach:

• Identify where your pipeline currently loses the most time or quality signal.
• Pick the one or two integrations from this list that directly address that constraint.
• Instrument the before/after: measure cycle time, defect escape rate, or mean time to restore before and after implementing the integration.
• Use those results to justify and prioritise the next integration.

DevOps maturity is incremental. The teams that sustain the highest deployment frequencies with the lowest failure rates didn’t build their toolchains overnight. They built them deliberately, one well-justified integration at a time — and they measured the outcome of each one.

So go ahead: crank every dial to 11. Just make sure every dial is wired up correctly first.