Google Cloud offers a wide range of DevOps services designed to enhance the development, deployment, and management of applications. These services enable organizations to automate workflows, improve collaboration, and achieve continuous delivery with a high degree of efficiency. Google Cloud’s DevOps tools are tightly integrated, providing a seamless experience for teams adopting modern software development practices.

In this post, we will explore several of the most prominent DevOps services available on Google Cloud, highlighting their major features and how they contribute to accelerating software delivery and improving overall operational efficiency.

1. Google Cloud Build

Major Features:

• Fully Managed CI/CD Service:
  • Google Cloud Build is a fully managed continuous integration and continuous delivery (CI/CD) service that automates the process of building, testing, and deploying applications. It supports a wide range of programming languages and frameworks, allowing teams to build and deliver software quickly and securely.
• Integration with Google Cloud Services:
  • Cloud Build integrates seamlessly with other Google Cloud services, such as Google Kubernetes Engine (GKE), Cloud Functions, and Cloud Storage, making it easier to deploy to Google Cloud environments.
• Custom Build Configurations:
  • With Cloud Build, teams can define custom build configurations using YAML or JSON files. This allows flexibility in how builds are executed, from simple tasks to complex workflows.
• Speed and Scalability:
  • Cloud Build scales automatically based on the size of the build workload, ensuring that large applications are built in a fraction of the time without requiring additional infrastructure management.
• Security and Audit Logging:
  • Cloud Build provides built-in security features like Google Cloud Identity and Access Management (IAM) for access control, and audit logs to track changes and ensure compliance with security standards.

2. Google Kubernetes Engine (GKE)

Major Features:

• Managed Kubernetes Service:
  • Google Kubernetes Engine (GKE) is a managed service that simplifies the deployment, management, and scaling of containerized applications. It leverages Kubernetes, the most popular container orchestration platform, allowing organizations to run applications in a cloud-native environment.
• Seamless Integration with DevOps Pipelines:
  • GKE integrates seamlessly with Google Cloud Build and other DevOps tools, enabling teams to automate the deployment of containerized applications as part of their CI/CD pipeline.
• Automatic Scaling and Self-Healing:
  • GKE provides automatic scaling of workloads based on traffic and resource usage. It also includes self-healing features, such as automatic restarting of failed containers, ensuring high availability and reliability.
• Security and Compliance:
  • GKE offers robust security features such as IAM integration, role-based access control (RBAC), and built-in encryption, helping teams protect sensitive data and maintain compliance.
• Multi-Region and Hybrid Deployment:
  • GKE supports multi-region deployments, allowing teams to distribute their applications globally for better availability and performance. It also supports hybrid cloud deployments for running applications across on-premises and cloud environments.

3. Google Cloud Operations (Formerly Stackdriver)

Major Features:

• Comprehensive Monitoring and Logging:
  • Google Cloud Operations (formerly Stackdriver) provides a suite of tools for monitoring, logging, and diagnostics. It collects and visualizes performance metrics, logs, and traces from applications, giving teams real-time insights into their systems’ health.
• Integrated Error Reporting:
  • Google Cloud Operations integrates with Google Cloud’s logging and monitoring systems to provide real-time error reporting. It helps teams identify issues in production and resolve them before they impact end-users.
• Custom Dashboards:
  • Teams can create custom dashboards that visualize system performance and track key metrics. Cloud Operations allows teams to tailor their dashboards based on specific needs, improving overall observability.
• Alerting and Notifications:
  • Cloud Operations supports setting up automated alerts based on predefined thresholds, notifying the team when anomalies occur in system performance or when errors need immediate attention.
• End-to-End Traceability:
  • With Cloud Trace and Cloud Profiler, teams can trace requests through microservices, analyze performance bottlenecks, and optimize application latency, all from within the Google Cloud platform.

4. Google Cloud Deployment Manager

Major Features:

• Infrastructure as Code (IaC):
  • Google Cloud Deployment Manager allows teams to manage their Google Cloud resources using configuration files written in YAML, JSON, or Python. This Infrastructure as Code (IaC) approach enables teams to automate infrastructure provisioning and management.
• Resource Templates:
  • Deployment Manager enables the use of reusable resource templates to define Google Cloud resources. This makes it easy to deploy and manage resources consistently across different environments, reducing manual configuration errors.
• Version Control and Auditing:
  • Cloud Deployment Manager supports versioning of configuration files, allowing teams to maintain an audit trail of infrastructure changes. This is essential for tracking changes over time and maintaining compliance.
• Automated Rollbacks:
  • In the event of deployment failure, Deployment Manager automatically rolls back changes to ensure that the system remains in a stable state. This ensures minimal disruption to users.
• Multi-Environment Support:
  • With Deployment Manager, teams can manage different environments (e.g., development, staging, production) by using environment-specific configurations, making it easier to replicate infrastructure consistently across multiple environments.

5. Google Cloud Source Repositories

Major Features:

• Private Git Repositories:
  • Google Cloud Source Repositories provides fully managed, private Git repositories for storing and managing code. It integrates seamlessly with Google Cloud tools, including Google Cloud Build and Google Kubernetes Engine (GKE).
• Scalability:
  • Cloud Source Repositories can scale with your organization’s needs, handling everything from small repositories for individual projects to large, enterprise-level codebases.
• Integration with Other Google Cloud Services:
  • Cloud Source Repositories integrate with Google Cloud Build, allowing teams to trigger builds automatically when code changes are pushed to the repository, streamlining the CI/CD process.
• Collaboration and Code Review:
  • With built-in Git support, Cloud Source Repositories allows for team collaboration through features like pull requests and code reviews. This enhances team productivity and ensures high-quality code before deployment.
• Security and Access Control:
  • Cloud Source Repositories are secured using Google Cloud IAM, ensuring that access is tightly controlled and only authorized users can make changes to the repository.

6. Google Cloud Artifact Registry

Major Features:

• Centralized Storage for Artifacts:
  • Google Cloud Artifact Registry is a fully managed service for storing and managing software artifacts such as Docker images, Helm charts, and language-specific packages. It serves as a central repository for artifacts in DevOps workflows.
• Secure Access and Storage:
  • Artifact Registry provides integrated access control through IAM policies, ensuring secure storage and access to artifacts. This is critical for maintaining the integrity and security of software components.
• Integrated with Google Cloud Build:
  • Artifact Registry integrates seamlessly with Google Cloud Build, allowing teams to push built artifacts directly into the registry as part of their CI/CD pipeline. This facilitates efficient and secure artifact management.
• Multi-format Support:
  • Artifact Registry supports multiple formats, including Docker, Maven, npm, and Python, making it a versatile solution for teams working with various programming languages and containerized applications.
• Regional Repositories:
  • Teams can choose to store artifacts in specific Google Cloud regions, ensuring that artifacts are stored closer to the target environments, improving access speed and compliance with data residency regulations.

The Power of DevOps Services on Google Cloud

Google Cloud offers a comprehensive suite of DevOps services that help teams automate, collaborate, and deliver high-quality software at scale. From CI/CD pipelines using Google Cloud Build to infrastructure management with Google Cloud Deployment Manager, and monitoring with Google Cloud Operations, these services streamline the software development lifecycle and improve operational efficiency.

By integrating Google Cloud’s powerful DevOps tools, organizations can improve collaboration between development and operations teams, enhance software quality, and accelerate time-to-market. The flexibility, scalability, and security provided by Google Cloud services ensure that DevOps teams can build, deploy, and manage applications with confidence.

Amazon Web Services (AWS) provides a robust set of tools and services that support the implementation of DevOps practices, enabling organizations to automate and streamline their software development, deployment, and operations. AWS DevOps services provide a fully managed environment to integrate continuous integration (CI), continuous delivery (CD), and continuous monitoring into the development lifecycle. These services empower teams to increase agility, improve software quality, and accelerate time-to-market.

In this post, we will explore the key AWS DevOps services, their major features, and how they contribute to a more efficient, scalable, and secure DevOps pipeline.

1. AWS CodePipeline

Major Features:

• Continuous Integration and Continuous Delivery:
  • AWS CodePipeline is a fully managed CI/CD service that automates the building, testing, and deployment of applications. It enables teams to define and visualize the stages of the software release process, ensuring that code changes flow smoothly from development to production.
  • By automating the pipeline, CodePipeline allows for faster and more reliable software releases, improving the efficiency of the deployment process.
• Seamless Integration with AWS Services:
  • CodePipeline integrates seamlessly with other AWS services like AWS CodeCommit, AWS CodeBuild, and AWS CodeDeploy, creating a unified platform for the entire DevOps lifecycle.
  • This tight integration ensures smooth automation of tasks such as source control, code building, testing, and deployment to AWS environments.
• Customizable Pipelines:
  • CodePipeline provides the flexibility to define custom workflows using a range of AWS services and third-party tools. Teams can configure multiple stages and actions (e.g., manual approval, tests, or code deployments) to align with their specific DevOps needs.
• Parallel Execution:
  • With AWS CodePipeline, teams can execute multiple pipeline actions in parallel, speeding up the process of releasing new software versions.

2. AWS CodeBuild

Major Features:

• Fully Managed Build Service:
  • AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages ready for deployment. It eliminates the need to set up, manage, and scale your own build servers, reducing infrastructure overhead.
• Scalability:
  • CodeBuild automatically scales to meet your build volume. As a result, it can handle any number of builds, whether it’s one per day or hundreds per minute, without requiring manual intervention or infrastructure management.
• Integration with CodePipeline:
  • CodeBuild works seamlessly with AWS CodePipeline, allowing automatic triggering of builds as part of the CI/CD pipeline. This ensures that each code commit is tested and built consistently before moving through to deployment stages.
• Support for Multiple Programming Languages:
  • CodeBuild supports a wide variety of programming languages, such as Java, Python, Node.js, and Go, along with the ability to use custom Docker images for building applications.
• Custom Build Environments:
  • With AWS CodeBuild, you can create custom build environments using Docker containers, allowing you to easily manage dependencies and ensure consistency across builds.

3. AWS CodeDeploy

Major Features:

• Automated Deployment:
  • AWS CodeDeploy automates the deployment of applications to Amazon EC2 instances, on-premises servers, and Lambda functions. It supports deployment strategies like blue/green deployments and canary releases, which minimize downtime and reduce the risk of deployment failures.
• Elasticity:
  • CodeDeploy automatically scales to deploy updates to multiple instances or Lambda functions simultaneously, enabling large-scale deployments across distributed environments.
• Deployment Strategies:
  • The service offers flexibility with deployment strategies. For example, blue/green deployments enable rolling back to the previous version if an issue arises with the new version, while canary deployments allow a small subset of users to access the new version before full deployment.
• Application Monitoring:
  • AWS CodeDeploy integrates with CloudWatch to provide real-time monitoring and logging, helping teams track the success or failure of deployments and take action quickly if issues arise.
• Cross-Platform Support:
  • CodeDeploy can deploy applications on multiple platforms, including AWS EC2 instances, Lambda, and on-premises servers, providing flexibility in deployment environments.

4. AWS CloudFormation

Major Features:

• Infrastructure as Code (IaC):
  • AWS CloudFormation allows teams to model and provision AWS resources using templates written in JSON or YAML. This allows DevOps teams to manage and provision AWS infrastructure through code, ensuring consistency and reproducibility across environments.
• Simplified Resource Management:
  • CloudFormation enables users to create, update, and delete resources in an organized and predictable manner. By managing infrastructure through templates, it eliminates the need for manual configuration, reducing human error and speeding up deployment.
• Integrated with DevOps Tools:
  • CloudFormation integrates seamlessly with other AWS DevOps services like CodePipeline, enabling full automation of infrastructure provisioning within the CI/CD pipeline.
• Stack Management:
  • CloudFormation allows teams to group related AWS resources into stacks, making it easier to manage and update multiple resources as a single unit.
• Change Sets and Rollback:
  • CloudFormation provides change sets that show potential changes before applying them to production, helping avoid errors. If issues arise, CloudFormation supports rollback to the previous stable state, ensuring that infrastructure changes do not break existing systems.

5. Amazon CloudWatch

Major Features:

• Real-Time Monitoring and Logging:
  • Amazon CloudWatch provides real-time monitoring and logging for AWS resources, applications, and services. It collects and tracks metrics such as CPU usage, memory, network activity, and custom application metrics.
• Automated Alerts and Notifications:
  • CloudWatch enables the setup of automated alerts to notify teams when system performance deviates from expected thresholds. Notifications can be sent via email, SMS, or even trigger automated actions to scale resources or fix issues.
• Centralized Log Management:
  • CloudWatch integrates with other AWS services like CloudTrail, Lambda, and Elastic Load Balancing to provide centralized logging. This helps DevOps teams with troubleshooting by aggregating logs from different sources and offering real-time insights into application behavior.
• Application Performance Monitoring:
  • With CloudWatch, DevOps teams can monitor application performance and track detailed metrics for better decision-making. By integrating CloudWatch with other AWS services, teams can have a comprehensive view of infrastructure and application health.
• Automated Scaling:
  • CloudWatch can trigger Auto Scaling actions based on real-time performance metrics, ensuring that applications remain highly available and scalable without manual intervention.

6. AWS Lambda

Major Features:

• Serverless Computing:
  • AWS Lambda is a serverless compute service that enables developers to run code without provisioning or managing servers. Lambda automatically scales to handle requests and only charges for actual execution time, making it highly cost-effective.
• Event-Driven:
  • Lambda is designed for event-driven applications. It automatically triggers functions in response to various AWS services and external events, such as HTTP requests via API Gateway or file uploads to S3.
• Seamless Integration with DevOps Pipelines:
  • AWS Lambda integrates well with other DevOps services like CodePipeline and CodeDeploy, enabling serverless applications to be easily deployed as part of a CI/CD pipeline.
• Cost Efficiency:
  • Since Lambda only charges for the actual compute time, it is a highly cost-effective solution for event-driven tasks and microservices architectures. Organizations can deploy scalable applications without worrying about idle server costs.
• Easy Integration with AWS Services:
  • Lambda integrates with numerous AWS services such as S3, DynamoDB, SNS, and SQS, enabling teams to build highly scalable, cloud-native applications quickly.

Leveraging AWS for DevOps Success

AWS provides a comprehensive set of services for implementing DevOps practices that help organizations automate software development, testing, deployment, and monitoring. Services like AWS CodePipeline, AWS CloudFormation, AWS Lambda, and Amazon CloudWatch enable seamless integration, real-time monitoring, and efficient infrastructure management.

By leveraging these tools, DevOps teams can achieve faster software delivery, improved collaboration, and greater scalability. AWS DevOps services empower teams to streamline their workflows, reduce operational overhead, and ensure high availability and security across cloud environments.

Introduction: Jaipur, the Pink City, is a blend of royal heritage and modern vibrancy, making it one of the most exciting places to explore. Whether you’re planning to visit iconic monuments or take a leisurely drive around the city, Motoshare’s newly launched bike and car rental services in Jaipur make exploring the city easier than ever. With just a few clicks, you can rent a bike or car directly from local vehicle owners and enjoy the freedom to explore Jaipur at your own pace.

An Overview of Motoshare Services

Motoshare provides a seamless and efficient bike and car rental experience in Jaipur. Their online platform allows renters to browse a wide variety of vehicles, ranging from bikes for adventurous city tours to comfortable cars for family trips. Motoshare’s service offers the flexibility to rent a vehicle for as long as you need, without the hassle of traditional rental services.

Booking a vehicle is incredibly easy – simply choose your preferred vehicle, confirm your booking, and enjoy the convenience of having it delivered to your location. Motoshare’s platform is designed to be user-friendly, ensuring that the booking process is quick and hassle-free.

For more details:

• Car Rentals in Jaipur: Motoshare Car Rentals
• Bike Rentals in Jaipur: Motoshare Bike Rentals

Top 10 Places to Visit in Jaipur After Renting a Bike or Car

1. Amber Fort A magnificent fort with a blend of Hindu and Mughal architecture. Visit in the morning to avoid the crowds and enjoy the stunning views.
2. City Palace The royal residence of Jaipur’s former rulers, known for its beautiful courtyards and museums. It’s best to visit in the early afternoon when the palace is less crowded.
3. Hawa Mahal Known as the “Palace of Winds,” this architectural marvel is perfect for a photo-op. The best time to visit is during sunset for beautiful lighting.
4. Jantar Mantar is A UNESCO World Heritage site and an astronomical observatory. It’s best to visit in the late morning when the sun isn’t too harsh.
5. Jaigarh Fort A historical fort with panoramic views of Jaipur. Renting a car is ideal for this location as it’s a bit farther from the city center.
6. Albert Hall Museum A beautiful museum that showcases Rajasthan’s rich history and art. Visit in the late morning or early afternoon.
7. Nahargarh Fort A popular spot to enjoy the sunset views of the city. It’s best visited in the evening.
8. Jal Mahal A picturesque palace in the middle of Man Sagar Lake. Take a peaceful drive around the lake in the early morning.
9. Sisodia Rani Garden A peaceful retreat with terraced gardens and fountains. It’s ideal for a relaxing afternoon visit.
10. Birla Mandir A stunning temple made of white marble, best visited during sunrise or sunset for a serene experience.

Advantages of Motoshare

Renting a vehicle with Motoshare in Jaipur comes with numerous benefits:

• Seamless Online Booking: The platform is easy to navigate, allowing you to book bikes and cars quickly and conveniently.
• Cost-Effective: Renting directly from local owners offers competitive pricing with no hidden fees.
• Wide Range of Vehicles: Motoshare offers a variety of vehicles, from bikes to cars, catering to different needs and budgets.
• Flexible Rental Duration: Whether you need a vehicle for a few hours or several days, Motoshare provides flexible rental options.
• Local Insights: Renters can get personalized travel tips from local vehicle owners, making your trip even more special.

Hello-tech lover, welcome to the visualizing path of programming. You know better today trends to take up-gradation in technologies and their environment around the world. Through this article, we are going to give you lessons on Gitlab runner and we will go very basic step by step and learn how to install, register, and start Gitlab runner. So, if you have no primary knowledge of that then do not worry. We will start from scratch.

What is a Gitlab runner?

Gitlab runner is used in Gitlab CI and you have not learnt yet Gitlab CI. Now, this is an open-source continuous integration service. It is included with Gitlab, is used to run jobs & send results back to Gitlab. Now, let’s see, what are the steps you have to do for setting up Gitlab runner.

How to install Gitlab runner?

Let us see, what we will do about this? You will go to Google and search for Gitlab runner. You will visit the website for Gitlab runner documentation and you will see all the documents over there. This is written in a go programming language, you can see some features like multiple jobs concurrently run. And if you will have to move down, there will be a section of install Gitlab runner. You would find every sort of documentations about Gitlab runner like how to configure, troubleshooting, etc. If you will click on the install option then there will be options like Mac for the operating system. So, you will click on the option to install Mac operating system and there you will have the steps if you want to install it manually. Then you will have to install the binary in your system and you can use that given command from there. You may copy and run it in your terminal then it will download the binary. So, you will have to give permissions from the given command. This is the first way, the second way is homebrew installation. If you will go for homebrew, there will be a website so, you will see there “the missing package manager for Mac OS” like this type of thing will be written there. To install homebrew, it is very simple so, you will have to copy that statement from there and will have to run on terminal. Then you can start using homebrew, it is a good way to install and manage packages on Mac. There will be a command so, you will have to run that on your terminal then this will install Gitlab runner on your Mac operating system. Here are some commands to install, version, start, and stop like

Brew install Gitlab –runner

Gitlab-runner –version

Brew services start GitLab –runner

Brew services stop GitLab –runner

How to register Gitlab runner?

Let us see how can we register GitLab runner? So, there you will visit the Gitlab runner documentation. You will find the registration part in that you will have a register link and from there, you would see that what are the step to register the Gitlab runner? Now, on that page, if you will scroll down then you will find all the steps for Mac OS, window OS, etc. so, you will have to run the following command Gitlab –runner register, this is the first command. You will go to your terminal and run the command Gitlab –runner register then there, they shall ask for a Gitlab-ci token for this runner. So, to get the ci token, you will log in to your Gitlab account and you will have to take tokens from Gitlab projects. You will go to your project by doing a sign-in process then you would have to go to project settings. If you will see, scroll down and you will find the setting there. If you will go to settings under that you will get CI /CD section. You will go to CI/CD then you will have a section of runner then you will expand it. You will find the setup runner manually option will be there and there will be a token of your project. You will have to do copy that from there and provide it on the terminal and you will also put tags for your Gitlab ci like ssh, ci. Then you will see registering runner…..succeeded and after this, you will have to enter the name of executors like parallels, custom, Docker-ssh, etc. you have so many executors if you want to run on Docker and some Kubernetes. If you will put simple executor shell and press enter button. Then it will show the runner registered successfully. These are the commands which you have followed.

How to start Gitlab runner?

So, if you have to start Gitlab runner then you can use these brew services start Gitlab –runner. To start Gitlab runner, if you’re not using brew then you will have all the options there like Gitlab –runner stop. You can take Gitlab –runner start if you are not using brew. Then it will be successfully started and the next step will be to check runner is activated in the project. So, you will have to check runner is activated or not, again, you will go to your Gitlab, project, and in settings CI/CD. And there you will expand the runner section, if you will scroll down then you will see your runner will be there, it is started. You can also see the name and text that you had added. So, you have seen that how to use the runner and how to do Gitlab ci? I wish, these lessons will be very helpful for you.

Related video:

 Today, IT and businesses often find themselves in silos working separately without a shared vision of the customer experience and all the services supporting it. IT must move beyond maintaining up a time to deliver the agility and competitive edge the business needs this means better cohesion between IT and business using predictive tools to prevent service failure meet Splunk IT service intelligence.

 IT service intelligence teams can no longer afford to be reactive especially when it comes to performance and availability. A protective IT strategy is necessary to get ahead of a problem and to protect revenue and your customer experience. Splunk IT service intelligence delivers a predictive IT, running an AIs we have AI scalable data platform that can ingest, search, analyze, and all types of data. ITSI allows you to go from reactive to predictive IT while gaining insights across both IT and business services. Splunk ITSI allows customers to predict service degradation 30 to 40 minutes in advance with the proactive alert for the KPIs most at risk so you can resolve incidents before they start gaining an end-to-end view of both IT and business KPIs on a single screen so you can work on what matters most when it comes to AI ops strategy. ITSI helps you become more proactive and even predictive reduce your incident investigation time business impact outages and event noise ensure your best customer experiences yes and keep the business and IT working better together with Splunk IT service intelligence. Put AI at the heart of your operations and try the free ITSI sandbox from today.

What are the benefits of Splunk IT Service Intelligence?

From the start as we are learning the series of Splunk software tools. In this stack, we discuss that every software has its own performances, capabilities, and security management but how the software works predictive and analytic. This is the reason behind endorsing the system software like how fast service is happening, and how the software is managing by the ITs team. With this effect, there are certain benefits related to Splunk ITSI admin as:-

• Intelligent incident management: Accelerate MTTR using real-time event correlation, automated incident prioritization and integrations with IT service management (ITSM) tools.
• Advance analytics & alerting: It manages a certain function as anomaly detection, adaptive thresholding, and predictive alerts & dashboards.
• Service analyzer: Service monitoring dashboards that display dependent systems and apps. Drill- down into a service’s underlying components to determine what’s impacting its health score.
• End to End visibility: protect business performance by monitoring service health, drill down into underlying infrastructure, all from one dashboard. 

Why we should learn Splunk IT Service Intelligence?

Nowadays, in this trending world. You have met with so many tools in the IT software services. Whenever we talk about Splunk’s advanced services then intelligence plays a prominent role in the IT entities. Now, we are talking about the learning conditions for IT service intelligence. So, what are the reasons to implement a Splunk ITSI? Before we had traditional solutions and those solutions were created as operational challenges like lack of visibility, complex environment, & slow response times. The most important talk is that lack of intelligence service created a significant impact on a business like lost revenue, inefficient productivity, poor customer experiences, & struggle to innovate then it creates technical advance in the field of IT sectors on the basis of Business and IT Service Monitoring, Intelligent Incident Management, Machine Learning, and Predictive Analytics. I wish that these awareness scenarios will give you brain mapping concepts for the Splunk intelligence certified admin.

How our Splunk IT Service Intelligence course/training would help?

This tutorial will help you about understanding the core features of Splunk ITSI like IT event management, service monitoring, and advanced analytics & alerting. It will give you a balancing ideology in the field of KPIs, Data sources, entities, services, incident response, automated remediation, predictive analytics, and event analytics. Through these interactive lectures and hands-on challenges, you will be able to learn to use IT service intelligence, services & glass tables, Notable event & event aggregation policies, and deep dives-investigation with swim lane views. After this, it gives you certification and that makes you qualified for the job opportunities along with the expected high-paid salary and makes your life efficient with future scope.

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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.

Global Agile and DevOps Services Software Market Market to Witness Robust Expansion Throughout the Forecast Period 2021–2030. Here are a few factors that will accelerate the growth of the Agile and DevOps Services Software industry.

Technologies don’t rest on their laurels but rest on how they are gradually changing our lives and introducing the latest, innovative solutions to existing problems. The global Agile and DevOps Services Software market now needs a new format of doing business. Market.biz has all the necessary tools to make the Agile and DevOps Services Software business data available to everyone, regardless of distance and region.

The Agile and DevOps Services Software Market study offers an in-depth analysis of market trends, drivers, Sales, Segmentation, restraints, opportunities, Revenue, etc. Along with qualitative information, this report includes the quantitative analysis of various segments in terms of market share, growth, opportunity analysis, market value, etc. for the forecast years 2021-2030. The Agile and DevOps Services Software Market is segmented on the basis of type, application, and geography, and competitiveness.

INDUSTRY TREND

The global Agile and DevOps Services Software industry continues to expand rapidly. However, regional performance varies, with some regional Agile and DevOps Services Software markets approaching near-mainstream status, while others remain stuck in neutral. Overall, global Agile and DevOps Services Software-sales volumes are becoming large enough to create substantial profit pools for well-positioned suppliers and other upstream players– but they are also having a negative impact on traditional Agile and DevOps Services Software profit margins. The entire Agile and DevOps Services Software value chain continues to recalibrate as industry giants follow different sourcing strategies and as many incumbents, plus new suppliers, enter the market. In the current highly competitive environment, the ultimate winners have yet to be determined. With the breakeven for Agile and DevOps Services Software still, a few years away, giants are feeling the heat. To accelerate and ensure sustainable, profitable growth, the Agile and DevOps Services Software industry still needs to overcome several challenges.

Segments Covered in the report

This report forecasts revenue growth at a global, regional & country level, and provides an analysis of the industry trends in each of the sub-segments from 2020 to 2030. For the purpose of this report, stats and data have segmented the global Agile and DevOps Services Software Market on the basis of end-users, type, and region:

Companies considered and profiled in this market study

Atlassian; Microsoft; Rally (now CA Technologies); Microsoft; CollabNet VersionOne; Jama Software; IBM; Perforce Software; Broadcom (CA Technologies); PTC; Siemens; Rocket Software; Parasoft; Micro Focus; Original Software; Beesion; Intland Software; Kovair Software; Micro Focus; Digite;

The companies have adopted various strategies, including mergers, acquisitions, and partnerships to hold ongoing trails and come up with new developments in the Agile and DevOps Services Software market.

End Users Outlook (Revenue, USD Billion; 2021-2030)

Large Enterprises (1000+ Users); Medium-Sized Enterprise (499-1000 Users); Small Enterprises (1-499 Users)

Type Outlook (Revenue, USD Billion; 2021-2030)

Cloud-Based; On-Premises

Regional Outlook (Revenue, USD Billion; 2021-2030)

North America
(Panama, Mexico, Barbados, United States, Canada, Puerto Rico, Trinidad, and Tobago, etc).

South and Central America
(Brazil, Chile, Argentina, Belize, Costa Rica, Panama, Guatemala, El Salvador).

Europe
(Spain, Netherlands, Germany, Sweden, Switzerland, San Marino, Ireland, Norway, Luxembourg, etc).

Asia-Pacific
(United Arab Emirates, Qatar, China, India, Hong Kong, Korea, Israel, Australia, Singapore, Japan, Kuwait, Brunei, etc).

The Middle East and Africa
(Egypt, Algeria, Nigeria, South Africa, Angola, Saudi Arabia, Bahrain, Oman, Turkey, Lebanon, etc).

KEY COVERAGE OF THE REPORT

1. Region and country-wise data of the global Agile and DevOps Services Software market from the period 2016-2026. While 2016 to 2018 has been utilized as historical data, 2019 is considered the base year. 2020-2026 has been derived as forecasts.

2. Various types of alternatives available have been analyzed. Statistical analysis has been conducted on obtaining the individual share of these segments.

3. The regulatory framework of each region. Regional up-coming research and application outlook. The regional prevalence of Agile and DevOps Services Software has been mapped.

4. Status of ongoing developments. Demand and Supply GAP Analysis

5. Regional Competitors Pricing Strategy. Market share analysis of the top industry players. Strategic recommendations for the new entrants

6. Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)

7. Strategic recommendations in key business segments based on the market estimations

8. Competitive landscaping mapping the key common trends. Company profiling with detailed strategies, financials, and recent developments.

On Thursday, Judge Barbara Jacobs Rothstein of the Western District of Washington issued an order denying Parler’s request for Amazon Web Services (AWS) to reinstate its web-hosting services; however, Parler has not received the relief it requested.

Parler sued AWS for suspending its use of Amazon’s cloud hosting services, which it claimed would “kill” its business. Parler alleged violations of the Sherman Act for conspiracy in restraint of trade, breach of contract, and tortious interference with business expectancy. As mentioned, Parler asked for a temporary restraining order (TRO) for the court to order AWS to reinstate its web-hosting services for Parler. AWS opposed the TRO motion, contending that Parler failed to satisfy the requirements for this request — specifically, that it is Parler, not AWS, that violated the agreement.

The court noted that after various court requests and briefings, the parties agreed that the motion should be converted to a motion for preliminary injunction. The court reiterated what this case is not about and added that since this is before discovery, Parler has sought for the court to “determine the likelihood that Parler will ultimately prevail on its claims, and to order AWS to restore service to Parler pending a full and fair litigation of the issue raised in the Complaint.”

The court found that, at this point, Parler has failed to show that it is likely to succeed on the merits. The court also considered if Parler would suffer irreparable injury. The court noted that Parler averred that “AWS’s suspension or termination renders Parler unable to deliver the services it promises its users, and ‘entirely unable to function online.’” The court asserted that Parler’s purported injuries “may be irreparable”; however, the court stated that the Supreme Court “explicitly rejected the ‘possibility’ of irreparable harm as ‘too lenient’ to support a preliminary injunction.” The court added that AWS “disputed that Parler has shown that its extinction is ‘likely’ in the absence of an injunction.” The court stated that it is not making a finding on this issue but that “Parler’s claims to irreparable harm are substantially diminished by its admission ‘that much of that harm would be compensable by damages.’ ” As a result, the court found that Parler has not sufficiently shown the likelihood of irreparable injury needed for a preliminary injunction. The court added that the motion does not raise “serious question” on the merits of the claims and the balance of hardships is not in Parler’s favor. Additionally, the court stated that the balance of equities and the public interest does not weigh in favor of granting the injunction, particularly in light of the allegedly violent and abusive content that was on Parler.

As a result, the court found that Parler failed to meet the requirements to issue a preliminary injunction.

Google will open a “cloud region” where it can provide its cloud services in Saudi Arabia via a joint venture with state-owned oil producer Saudi Aramco, as Covid-19 prompts a landmark acceleration in digital adoption across the Gulf.

Aramco’s Statement
“The collaboration taps into rapidly expanding cloud services demand in Saudi Arabia, which is forecast to reach a market opportunity up to $10 billion by 2030,” Aramco said in a statement on Monday.

Aramco said the agreement with Google will deliver “high performance, low-latency cloud services to enterprise customers in Saudi Arabia” as warming relations in the region open the political space for U.S. technology companies to expand into the kingdom.

Google-Saudi Aramco Deal
Google began negotiations with Saudi Arabia on the project in 2018, but talks reportedly stalled following the murder of journalist Jamal Khashoggi by state agents in the same year. Google Cloud is now moving ahead with plans to establish and operate a new cloud region in the kingdom. No financial terms were disclosed.

The technology agreement also shows Silicon Valley elites are comfortable doing public business with the regime, despite some concerns over Saudi Arabia’s global role as an exporter of fossil fuels and its poor human rights record.

Google said its investment into Saudi Arabia will allow businesses to grow and scale their offerings in the kingdom. The deal also boosts its cloud computing business, which brings in more than $10 billion in annual revenue for its parent, Alphabet Inc.

Who Are The Customers?
Regional e-commerce firm Noon and social media platform Snap were named as customers.

“In order to deliver a positive user experience, it is important to put our infrastructure as close as possible to our customers,” Karl D’Adamo, senior director for infrastructure at Snap Inc., said in a statement.

“With the new Google Cloud region in the Kingdom of Saudi Arabia, we will be able to securely deliver services to our nearby customers at higher speeds and with greater flexibility,” Hisham Zarka, CTO and managing director at Noon said.

Google competes with Microsoft and Amazon in the growing cloud-computing market globally. Google currently operates in 24 regions with 73 availability zones, less than its primary competitors Microsoft Azure and market leader Amazon Web Services.

Amazon Web Services launched its first cloud region in the Middle East in Bahrain last year, servicing top tier local clients such as Emirati bank Emirates NBD and rideshare service Careem. Microsoft launched its offering in the United Arab Emirates around the same time, making Google a later entrant to the regional scene.

Diversification Of Saudi Economy
Saudi Arabia has been actively courting Silicon Valley, seeking foreign investment to bolster its ambitious Vision 2030 reform plans and diversify its oil-dependent economy. The kingdom already invests significantly in American technology firms via its Public Investment Fund.

The new infrastructure will accelerate Saudi Arabia’s tech industry, which has one of the fastest-growing cloud adoption rates in the region, according to Oxford Business Group, which expects the new centers will boost usage further.

Saudi Arabia also marked a milestone last month following the crowning of the country’s first tech unicorn. Saudi Arabia’s STC Pay now plans to expand its financial services offering across the Gulf region, after achieving a billion-dollar “unicorn” valuation on the back of a deal with Western Union.

“The future of Saudi Arabia’s business transformation and growth depends on its ability to successfully leverage cloud services,” Ahmad Al Sa’adi, Aramco’s senior vice president of technical services, said. It’s thought the Google cloud solution would offer Saudi businesses increased access to artificial intelligence, smart analytics, data management, security and other Google services.

Other Developments
The growing tech ties in the Kingdom come amid reports Google was preparing to establish a fiber optic network that will connect Europe and India through Israel and Saudi Arabia. The project has the potential to link Saudi Arabia and Israel for the first time, in another sign of warming relations despite the two countries having no formal diplomatic ties.

The underwater cable project, called Blue Raman route, is said to be nearly 5,000 miles long and is expected to cost $400 million.

Saudi government authorities have not publicly given approval for the project. The Wall Street Journal, citing sources, said Google could seek support from telecom partners to finance the initiative and share infrastructure.

Google has also announced plans to launch a cloud computing center in Qatar, which has been subject to a regional blockade led by Saudi Arabia, the UAE, Bahrain and Egypt since 2017.

Synopsis
Google Cloud and Saudi Aramco will partner to deliver cloud infrastructure to Saudi Arabia, as Covid-19 accelerates rapid digital adoption in the Gulf.

The agreement paves the way for a rollout of high-performance, low-latency cloud services to meet rapidity rising demand in Saudi Arabia.

Technology is seen as critical in modernizing Saudi Arabia’s oil-dependent economy and could play a role in further thawing regional relations.

Orange Business Services has formed a global strategic collaboration agreement with Amazon Web Services (AWS).

The collaboration seeks to help enterprises accelerate their digital transformation and maximise on the benefits of the cloud for greater agility when adapting to the needs of its customers.

Specially, the two will develop new solutions in the areas of modernisation and migration, data analytics, innovation and security, all of which will form new products and services to expedite customers’ journeys to the cloud.

“We are delighted with the business opportunities this partnership will bring to us and to our customers,” said Stefan Kanis (pictured), senior vice president of the cloud business unit, Orange Business Services.

“This collaboration with AWS extends our relationship, leveling up our capabilities to simplify and accelerate customers’ cloud transformations. Customers appreciate having a combination of our cloud excellence to address their specific needs as well as our global vision with local care.”

As part of this agreement, a dedicated Cloud Center of Excellence will be built on AWS, coordinating the joint development of an extensive training and certification program for more than 3,000 Orange Business Services employees in the areas of cloud, cybersecurity, digital and data.

As a result, enterprises will now have access to a one-stop-shop for all their Orange and AWS requirements. Additionally, Orange has also become part of the AWS Channel Reseller Program, allowing it to resell AWS services to its customers.

BT announced it has launched a new generation of cloud-optimized managed services with the first offering based on VMware’s SD-WAN technology.

VMware’s VeloCloud SD-WAN technology gives customers a single view of their networks as well as a choice of management controls. Traditional wide area networking (WAN) required dedicated, proprietary vendor hardware to be installed at each customer site. BT’s new managed service is based on generic hardware that’s capable of supporting a choice of software-based networking solutions from different vendors.

The VMware SD-WAN service can be managed or co-managed remotely by BT on its new digital service platform. BT is also offering its customers consultations with its experts to find the best solution to fit their needs. They can also provide advise on security to meet each customer’s security requirements.

Over the past year or so, organizations have been migrating their SD-WAN services from the initial “do-it-yourself” (DIY) model to managed SD-WAN services. Other service providers, such as Orange Business Services, and Windstream, are also offering their customers white-glove managed SD-WAN services that deliver a variety of high-level service benefits, including a single pane of glass, technical consulting resources and advanced service delivery.

“Our new managed service further expands customers’ choice of software-defined networking solutions. Presently available with industry-leading VMware SD-WAN, it is delivered over our new, digital service platform,” said BT’s Andrew Small, director of global portfolio, in a statement. “The solution is fully supported by our consultancy services to help customers maximize the benefits of performance, agility and flexibility for their businesses.”

BT didn’t say what vendor, or vendors, it used to build its cloud-first, scalable network for the software-defined networking services.

Dating back several years ago, BT previously deployed SD-WAN solutions from Nuage Networks and Cisco, including Cisco IWAN and Cisco Meraki SD-WAN.

Earlier this year, BT added Dell Technologies’ universal customer premises equipment (uCPE) to its Dynamic Network Services roster in order to offer business customers more choices. BT will offer its multinational customers Dell’s Virtual Edge Platform (VEP) platform of networking-specific uCPE as a converged networking, IoT and IT application platform.

BT started offering its Dynamic Network Services portfolio several years ago to give business customers greater choice, security, resilience, service, and agility. The managed services include on-premises network functions virtualization (NFV) as well as IoT and IT applications to help customers with their digital transformations. In addition to Dell, BT has also worked with Cisco to enable its Dynamic Network Services.

AWS addresses this gap with a layer of advanced security services designed to provide intelligent threat detection, automated data protection, application-layer defense, and a unified view across your entire AWS environment. This article explains each of those services, what it does, and how they work together.

App Protection: AWS WAF and AWS Firewall Manager

The AWS Web Application Firewall (AWS WAF) monitors HTTP and HTTPS requests at the edges of your distributed application. It integrates directly with three AWS services that handle inbound traffic:

• Amazon CloudFront — AWS’s content delivery network
• Application Load Balancer (ALB) — routes HTTP/HTTPS traffic to backend services
• Amazon API Gateway — manages and secures API endpoints

You define rules based on criteria such as IP address ranges, geographic origin, query string parameters, or HTTP header values. AWS WAF evaluates each incoming request against those rules and either allows it through or blocks it. Real-time metrics are available so you can refine rules over time, and a single set of rules can be applied across multiple applications.

Pricing for AWS WAF is consumption-based: charges apply per web access control list (web ACL), per rule added to a web ACL, and per million web requests processed. The figures cited at the time of the original article were $5 per web ACL, $1 per rule per web ACL per month, and $0.60 per million web requests — but you should verify current pricing directly in the AWS WAF pricing page, as these figures may have changed.

AWS Firewall Manager is a companion service for organizations operating multiple AWS accounts or managing a large number of resources. Once you define a security policy, Firewall Manager automatically applies WAF rules to new accounts and resources as they are provisioned — eliminating the manual overhead of updating rules across accounts. A flat monthly fee applies per protection policy, with additional charges for the underlying web ACLs and rules it manages.

DDoS Mitigation: AWS Shield

AWS provides two tiers of Distributed Denial of Service (DDoS) protection through AWS Shield.

AWS Shield Standard

Shield Standard is automatically included at no additional cost for all AWS customers. It defends applications running on Elastic Load Balancing, Application Load Balancer, Amazon CloudFront, and Amazon Route 53 against the most common volumetric and protocol-based DDoS attacks — specifically those targeting the network and transport layers (OSI Layers 3 and 4).

AWS Shield Advanced

Shield Advanced extends protection to the application layer (Layer 7), detecting and mitigating more sophisticated attacks that Standard cannot address. Additional capabilities include near-real-time attack visibility, access to the AWS DDoS Response Team (DRT), and cost protection against scaling charges incurred during an attack.

Shield Advanced is a paid service with a significant monthly commitment. The pricing figures cited at the time of the original article (approximately $3,000/month with a 12-month commitment, plus data transfer charges) should be verified against current AWS Shield pricing before budgeting.

Intelligent Threat Detection: Amazon GuardDuty

Amazon GuardDuty is a continuous threat detection service that analyzes data from three sources within your AWS environment:

• AWS CloudTrail logs — API call activity across your account
• VPC Flow Logs — network traffic metadata within your VPCs
• DNS logs — domain lookup activity from EC2 instances

GuardDuty applies machine learning, anomaly detection, and integrated threat intelligence feeds to identify malicious activity — things like unusual API calls, unauthorized data access patterns, communication with known malicious IP addresses, or cryptocurrency mining behavior. Rather than dumping raw events onto a security team, it surfaces prioritized, actionable findings.

Crucially, GuardDuty findings can be routed to Amazon CloudWatch Events, which lets you trigger automated response workflows — for example, automatically isolating a compromised EC2 instance or revoking suspicious credentials.

Pricing is consumption-based: you pay per million CloudTrail events analyzed and per GB of VPC Flow Log and DNS log data analyzed. Rates vary by AWS region. Consult the GuardDuty pricing page for current figures, as the per-region rates in the original article may be outdated.

Automated Data Security: Amazon Macie

Amazon Macie is a fully managed service focused specifically on protecting sensitive data at rest. It uses machine learning to automatically discover, classify, and alert on sensitive data — including personally identifiable information (PII), credentials, financial records, and intellectual property.

Macie works by establishing a behavioral baseline for how data in your S3 buckets is typically accessed and moved. When it detects anomalies — unusual access patterns, unexpected data transfers, or newly public bucket permissions — it generates findings and sends them to Amazon CloudWatch Events for automated remediation. Findings can also be forwarded to SIEM platforms or Managed Security Service Provider (MSSP) tooling via standard integrations.

At the time the original article was written, Macie was limited to Amazon S3. AWS has since significantly rearchitected Macie; the current version offers broader S3 coverage and a revised pricing model. Check the Amazon Macie pricing page for current details rather than relying on the figures in the original article.

Single-Pane Visibility: AWS Security Hub

AWS Security Hub aggregates and normalizes security findings from across your AWS environment into a single, unified view. It ingests findings from:

• Amazon GuardDuty — threat detection findings
• Amazon Inspector — automated vulnerability assessment results for EC2 instances and container images
• Amazon Macie — sensitive data findings
• AWS Partner Network (APN) solutions — third-party security tools that integrate with the Security Hub API

Security Hub classifies and prioritizes these findings, presenting them through dashboards, graphs, and tables that make it easy to spot trends and act on the highest-priority issues. Beyond aggregation, Security Hub also runs continuous compliance checks against established security standards — including the AWS Foundational Security Best Practices and the CIS AWS Foundations Benchmark — so teams can track their compliance posture over time without manual audits.

How These Services Work Together

Each of these services is useful independently, but their real value emerges when they are used in combination. A typical layered architecture might look like this:

1. AWS WAF and Firewall Manager block malicious HTTP/HTTPS traffic at the application edge, before it reaches your services.
2. AWS Shield absorbs volumetric DDoS attacks at the network and transport layers.
3. Amazon GuardDuty continuously analyzes API, network, and DNS logs to detect threats that do get through — compromised credentials, lateral movement, or C2 communications.
4. Amazon Macie monitors your S3 data stores for sensitive data exposure or anomalous access patterns.
5. AWS Security Hub pulls findings from all of the above into a single prioritized view, correlates them, and drives compliance reporting.
6. Amazon CloudWatch Events acts as the automation layer, triggering Lambda functions or Systems Manager runbooks to respond automatically to findings from GuardDuty, Macie, or Security Hub.

Summary

AWS’s advanced security services address a real operational problem: the gap between having security data and being able to act on it. By applying machine learning and behavioral analytics to the event streams your AWS environment already generates, services like GuardDuty and Macie surface the signal from the noise. WAF and Shield provide active, automated defenses at the edge. And Security Hub ties the findings together into a coherent, compliance-aware picture that security teams can actually use.

The on-demand, consumption-based pricing of these services also means that smaller teams can adopt them incrementally — enabling capabilities like intelligent threat detection or centralized findings aggregation without the capital expenditure that equivalent on-premises tooling would require. However, at scale, costs can accumulate quickly, so review current pricing pages for each service before designing your security architecture around them.

What Is the Service Desk Solutions Market?

Service desk software provides IT teams and support organizations with a centralized platform to log, track, manage, and resolve incidents, service requests, problems, and changes. The market spans a broad range of buyers — from small businesses using lightweight cloud-hosted tools to large enterprises running deeply customized ITSM (IT Service Management) platforms.

For DevOps teams, a well-integrated service desk is a critical operational layer. It bridges the gap between end users, support engineers, and development pipelines — feeding real-world incident data back into continuous improvement workflows.

Key Players in the Service Desk Solutions Space

A 2019 market research summary identified the following vendors as significant participants in the global service desk solutions market. Most remain active today, though some have rebranded or been acquired:

• ServiceNow — Enterprise-grade ITSM platform with deep workflow automation and integration capabilities. Widely used in large organizations.
• Jira Service Management (formerly Jira Service Desk, Atlassian) — Popular with DevOps and engineering teams due to native integration with Jira Software and Confluence.
• Freshservice (Freshworks) — Cloud-based ITSM tool aimed at mid-market organizations, known for ease of setup and a clean UI.
• ManageEngine ServiceDesk Plus — Feature-rich ITSM solution from Zoho’s ManageEngine division, available in both cloud and on-premises editions.
• Zendesk — Primarily a customer support platform, but widely used for internal IT helpdesks, especially in tech-forward companies.
• BMC Remedy / Helix — Long-established enterprise ITSM platform from BMC Software, commonly found in regulated industries.
• Cherwell IT Service Management — Codeless configuration platform focused on flexibility. (Note: Cherwell was acquired by Ivanti in 2021.)
• Agiloft — Highly customizable service desk and contract management platform.
• Samanage — Cloud-based IT service management tool. (Note: Samanage was acquired by SolarWinds and rebranded as SolarWinds Service Desk.)
• Spiceworks — Free, ad-supported helpdesk tool popular with small IT teams and MSPs.
• EasyVista — ITSM platform with a focus on low-code service management, popular in European markets.
• GoToAssist (LogMeIn) — Remote support and helpdesk tool, particularly useful for distributed teams.
• Track-It! (Ivanti) — IT helpdesk and asset management software aimed at small to mid-sized IT departments.
• Wolken — Cloud-native service management platform offering both IT and enterprise service management.
• Re:Desk — Lightweight helpdesk solution targeting smaller support teams.

Deployment Models: Cloud vs. On-Premises

Service desk solutions are typically offered in two primary deployment models:

Cloud-Based (SaaS)

The majority of modern service desk platforms are offered as Software-as-a-Service. Cloud-based tools offer faster deployment, automatic updates, lower upfront infrastructure costs, and built-in scalability. Vendors like Freshservice, Zendesk, and Jira Service Management are cloud-native or cloud-first.

On-Premises

On-premises deployments remain important for organizations with strict data sovereignty requirements, regulated environments (such as healthcare or government), or complex integrations with internal systems. ManageEngine ServiceDesk Plus and BMC Remedy offer mature on-premises options. Total cost of ownership is typically higher due to infrastructure and maintenance overhead.

Primary Use Cases and Verticals

Service desk solutions are used across a wide range of industries and functions. Key use cases include:

• IT Support — The core use case: incident management, request fulfillment, change management, and asset tracking for internal IT teams.
• Healthcare — Managing IT incidents for clinical systems, EHR platforms, and medical devices, often under compliance frameworks such as HIPAA.
• Education — Supporting students, faculty, and administrative staff with IT requests, software licensing, and device management.
• Enterprise Service Management (ESM) — Extending service desk capabilities beyond IT to HR, facilities, legal, and finance departments.
• DevOps Integration — Linking incident tickets directly to development workflows, enabling faster root cause analysis and feedback loops into CI/CD pipelines.

What DevOps Teams Should Look for in a Service Desk Tool

For engineering and DevOps organizations evaluating service desk platforms, the following capabilities are particularly relevant:

• API and webhook support — Essential for integrating with CI/CD pipelines, monitoring tools (PagerDuty, Datadog, Prometheus), and ChatOps platforms (Slack, Microsoft Teams).
• Incident-to-deployment traceability — The ability to link a production incident back to a specific deployment or code change reduces mean time to resolution (MTTR).
• Automation and workflow rules — Auto-routing, escalation policies, and SLA enforcement reduce manual overhead for on-call engineers.
• Integration with version control and project management — Native Jira/GitHub/GitLab integrations are valuable for teams that manage infrastructure as code.
• Self-service portals and knowledge bases — Reducing ticket volume by enabling users to find answers independently frees engineers for higher-value work.
• Reporting and analytics — Tracking incident trends, SLA compliance, and MTTR over time supports data-driven operations reviews.

Choosing the Right Platform

No single service desk platform is the right choice for every organization. Key factors to evaluate include:

• Team size and complexity — Lightweight tools like Spiceworks or Freshservice suit smaller teams; ServiceNow or BMC Helix are better matched to large, complex enterprise environments.
• Budget — Costs range from free (Spiceworks) to significant per-agent monthly fees for enterprise platforms. Factor in implementation and customization costs for on-premises or highly configured deployments.
• Existing toolchain — If your team lives in Atlassian tools, Jira Service Management is a natural fit. If you are a ServiceNow shop, extending its capabilities is often easier than introducing a second platform.
• Compliance requirements — Healthcare, finance, and government organizations should verify that their chosen platform supports the relevant compliance frameworks (SOC 2, HIPAA, FedRAMP, etc.).
• Vendor trajectory — Several vendors in this space have been acquired or consolidated. Review the current ownership and roadmap before committing to a long-term contract.

Summary

The service desk solutions market offers a mature and competitive set of options spanning cloud-native SaaS tools, flexible on-premises platforms, and enterprise ITSM suites. For DevOps and IT operations teams, the most important selection criteria go beyond feature checklists — tight integration with your existing toolchain, strong API support, and actionable analytics are what separate a service desk that reduces operational friction from one that adds to it. Evaluate platforms hands-on using free trials or community editions before committing, and prioritize vendors with active development roadmaps and strong community or enterprise support.

Cloud Computing as a Strategic Technology

Cloud computing has evolved from a niche IT concept into the backbone of modern enterprise infrastructure. As an umbrella term, it describes on-demand access to shared computing resources — servers, storage, databases, networking, software — delivered over the internet. Rather than owning physical hardware, organisations rent capacity from large-scale providers and pay only for what they use.

For developing economies, the promise is significant. Cloud services offer a cost-effective path to deploying and managing large volumes of IT resources without the capital expenditure required to build on-premises data centres. They also enable participation in newer paradigms such as blockchain, where participants need to record information in decentralised ledgers and access it remotely from multiple locations.

The State of Cloud Adoption in Bangladesh

Major cloud service providers — including AWS (Amazon Web Services), Microsoft Azure, Google Cloud, and Oracle — are active in Bangladesh through local partner companies. Demand is growing, particularly in the private sector, driven by rapid digital adoption across a large and increasingly connected population.

Microsoft has been publicly vocal about its ambitions in the market. At a Microsoft Cloud Innovation Summit titled “Transforming Bangladesh with Cloud” held in Dhaka, Sook Hoon Cheah, President of Microsoft Southeast Asia New Markets, highlighted the private sector as a primary growth area. Sonia Bashir Kabir, former Managing Director of Microsoft Bangladesh, pointed to the country’s scale — over 160 million people generating large volumes of data — as a major driver of business potential.

However, a key constraint shapes market strategy: the Government of Bangladesh restricts the hosting of government data outside the country. This limits cloud providers largely to private-sector engagements for now.

Key Barriers to Cloud Adoption

Several structural and regulatory challenges slow broader cloud adoption in Bangladesh and in similar developing markets:

• Low awareness: Many organisations, particularly small and medium enterprises, lack a clear understanding of what cloud computing is, how it works, and what risks and benefits it carries.
• Absence of a dedicated legal framework: No legislation directly governs or regulates cloud computing services. The Government of Bangladesh Information Security Manual (GoBISM) 2016, produced by the Bangladesh Computer Council under the ICT Ministry, provides guidelines and recommendations for government agencies, but its use is voluntary and not mandated across sectors.
• Cross-border data restrictions: Cloud infrastructure is inherently global. Data typically traverses and resides in servers outside the originating country. Bangladesh’s Bank Companies Act, 1991 (Section 12) restricts banks from removing records and documents related to their business to locations outside Bangladesh without prior approval from Bangladesh Bank. Whether this restriction applies to cloud-hosted data is legally ambiguous. Non-banking private organisations are not subject to the same explicit restriction, but the regulatory uncertainty itself creates hesitation.
• No comprehensive data privacy law: Without strong data privacy legislation, personal data hosted on cloud infrastructure may be vulnerable to unauthorised disclosure by cloud providers. This is both a legal gap and a reputational risk for organisations handling sensitive customer data.
• Cybersecurity exposure: Large-scale national and international cyberattacks remain a persistent threat. Without a national cybersecurity strategy tied to cloud adoption, organisations face significant residual risk.

Global Data Protection Standards as a Reference Point

Established economies have moved decisively to create regulatory frameworks that enable cloud adoption while protecting individual rights. Two prominent examples are worth examining:

The EU General Data Protection Regulation (GDPR)

The GDPR, which came into effect in May 2018, sets a comprehensive standard for how personal data must be collected, stored, processed, and transferred. It applies not only to EU-based organisations but to any entity processing data about EU residents. Key principles — purpose limitation, data minimisation, storage limits, security obligations, and rights of access and erasure — are directly relevant to how cloud providers must handle data on behalf of enterprise customers.

APEC Cross-Border Privacy Rules (CBPRs)

Developed by the 21 member economies of the Asia-Pacific Economic Cooperation (APEC) forum, the CBPR system provides a mechanism enabling organisations to demonstrate compliance with a common privacy standard when transferring data across borders within the region. It is designed to balance privacy protection with the practical need for data to flow freely to support digital trade and services.

The BSA Cloud Computing Scorecard

The Software Alliance (BSA) publishes a Global Cloud Computing Scorecard that ranks countries on their policy readiness for cloud adoption. The 2018 edition assessed policy areas including privacy law, security frameworks, cybercrime legislation, intellectual property protection, standards and interoperability, free trade in cloud services, and IT infrastructure.

The Scorecard found that most high-scoring countries had comprehensive data protection frameworks in place and had established independent privacy regulators. Several countries — including Brazil, Thailand, China, India, Indonesia, and Vietnam — were identified as having no comprehensive data privacy law or only very limited protections in place at the time of publication. Canada and Mexico ranked highest in the privacy dimension.

The clear implication: privacy law is not just a compliance requirement. It is an enabler of cloud adoption. Without it, organisations are reluctant to migrate workloads, and providers are reluctant to commit investment.

What a Sound Legal Framework for Cloud Adoption Should Include

For any government seeking to unlock the benefits of cloud computing — cost efficiency, scalability, innovation — while managing its risks, a comprehensive policy and legal framework is a prerequisite. That framework should address the following areas:

• Data privacy legislation: Define how personal data may be collected, processed, stored, and transferred, including rights for individuals to access, correct, and delete their data.
• Cross-border data transfer rules: Establish clear conditions under which data may be hosted or processed outside the country, replacing ambiguous restrictions with workable compliance mechanisms.
• Data encryption and security standards: Mandate minimum technical controls, including encryption at rest and in transit, access management, and regular security audits.
• Data backup, recovery, and archiving: Require cloud providers and enterprise users to maintain verifiable backup and recovery capabilities for critical data.
• Data portability: Ensure organisations can retrieve and migrate their data if they switch providers, avoiding lock-in and supporting competitive markets.
• Cybercrime legislation: Enact and enforce laws that criminalise unauthorised access, data theft, and sabotage of cloud infrastructure.
• National cybersecurity strategy: Define responsibilities across government agencies, cloud providers, and enterprise users for detecting and responding to incidents.
• Risk assessment requirements: Mandate that organisations conduct formal risk assessments before migrating to cloud services, and that providers verify controls are properly implemented before onboarding customers.
• Harmonisation with international standards: Align domestic rules with frameworks like GDPR and APEC CBPRs to reduce barriers to cross-border services and attract investment.

Avoiding Overregulation

A critical balance must be struck. Overly restrictive policies — those that effectively prohibit cross-border data flows or impose disproportionate localisation requirements — create trade barriers and stifle the cloud market rather than protecting citizens. Regulation should be designed to build trust and manage genuine risk, not to prevent adoption.

Cloud services’ value depends fundamentally on users trusting that their data will be handled as agreed. Providers must ensure that all controls are properly implemented before onboarding enterprise customers, and that data is not used or disclosed beyond the agreed purposes. Governments must create a legal environment where that trust is enforceable.

Summary

Cloud computing offers developing economies a genuine opportunity to modernise their IT infrastructure, reduce costs, and participate in global digital services. But the technology cannot deliver on that promise without a clear legal and regulatory foundation. Data privacy law, cross-border transfer rules, cybersecurity standards, and a risk-based compliance approach are not optional extras — they are the preconditions for sustainable, trustworthy cloud adoption. Policymakers who invest in getting this framework right will unlock far greater returns from cloud investment than those who leave the legal landscape ambiguous.

What Is the Mass Index?

The Mass Index is a technical indicator developed by Donald Dorsey in the early 1990s. Its core purpose is to detect potential trend reversals by tracking the widening and subsequent narrowing of the high-low price range over a rolling period — typically 25 days.

The underlying logic is straightforward: when a price range expands significantly and then begins to contract, the market may be setting up for a directional shift. Dorsey identified a reading above 27 as the key threshold — sometimes called the “reversal bulge.” Once the index climbs above 27 and then falls back below 26.5, a reversal signal is generated.

What the Mass Index Does Not Tell You

A critical point that traders sometimes miss: the Mass Index signals that a reversal may be coming, but it says nothing about direction. A rising Mass Index reading means volatility is increasing — the trading range is widening. It does not indicate whether the stock will reverse upward or downward. Traders typically combine the Mass Index with a trend-following indicator (such as a simple moving average) to determine the likely direction of any reversal.

Williams %R: Measuring Overbought and Oversold Conditions

The Williams Percent Range (Williams %R) is a momentum oscillator developed by Larry Williams. It measures where the current closing price sits relative to the high-low range over a specified lookback period — most commonly 14 days.

The scale runs from 0 to -100:

• Above -20: The stock may be considered overbought — price is near the top of its recent range.
• Below -80: The stock may be considered oversold — price is near the bottom of its recent range.
• Between -20 and -80: Neutral territory; no strong overbought or oversold signal.

In the example case, a 14-day Williams %R reading of -33.98 places the stock in neutral territory, closer to the overbought threshold than the oversold threshold — suggesting modest bullish momentum at the time of the reading.

Average Directional Index (ADX): Gauging Trend Strength

The Average Directional Index (ADX), developed by J. Welles Wilder, measures the strength of a trend rather than its direction. It is derived from the moving average of price range expansion over a given period, commonly 14 days. The ADX line oscillates between 0 and 100.

Standard interpretation guidelines:

• 0–25: Weak or absent trend — the market may be ranging.
• 25–50: Strong trend in place.
• 50–75: Very strong trend.
• 75–100: Extremely strong trend — rare in practice.

The example reading of 14-day ADX at 19.13 falls in the 0–25 range, indicating a weak or consolidating trend at the time of measurement. This context is important: a Mass Index reversal signal during a weak-trend environment carries different implications than one occurring during a strong trend.

Commodity Channel Index (CCI): Spotting Emerging Trends and Extremes

The Commodity Channel Index (CCI), developed by Donald Lambert, measures the current price relative to its statistical average over a specified period. Despite the name, CCI is widely applied to equities and other asset classes.

Key interpretation levels:

• Above +100: Price is significantly above its average — potential overbought signal or the start of a strong uptrend.
• Below -100: Price is significantly below its average — potential oversold signal or the start of a strong downtrend.
• Between -100 and +100: Price is within a normal range relative to its recent average.

The example 14-day CCI reading of 30.83 sits well within neutral territory, consistent with the weak ADX reading and suggesting no extreme price displacement at the time.

Moving Averages: Identifying Support, Resistance, and Trend Direction

Moving averages are among the most widely used tools in technical analysis. They smooth out price data by calculating the average closing price over a defined number of periods, making it easier to identify the underlying trend direction.

Common moving averages and their typical uses:

• 50-day MA: Medium-term trend reference; frequently used to identify support in uptrends.
• 200-day MA: Long-term trend reference; widely watched by institutional traders as a bull/bear dividing line.

Moving averages are lagging indicators — they reflect past price action rather than predicting future moves. Their value lies in confirming trend direction and identifying dynamic support and resistance zones. A 200-day MA reading of 0.01 (as cited in the example) indicates the stock was trading at extremely low price levels, which is an important broader context when interpreting all other indicators.

Relative Strength Index (RSI): Momentum and Divergence

The Relative Strength Index (RSI), also developed by J. Welles Wilder, is a momentum oscillator that measures the speed and magnitude of recent price changes. It oscillates between 0 and 100.

Standard thresholds:

• Above 70: Potentially overbought.
• Below 30: Potentially oversold.
• Between 30 and 70: Neutral momentum.

RSI is also used to identify divergences — situations where price makes a new high or low but RSI does not confirm it. These divergences can be early warnings of trend exhaustion.

In the example, 14-day RSI at 57.97, 7-day at 58.24, and 3-day at 57.45 all cluster in neutral-to-mildly-bullish territory. The consistency across timeframes suggests stable, unexceptional momentum — no overbought or oversold extreme, and no meaningful divergence between short and longer periods.

Combining Indicators: Building a More Complete Picture

No single indicator tells the full story. Each of the tools described above answers a different question:

Experienced traders rarely rely on a single signal. A Mass Index reversal bulge combined with an RSI approaching overbought territory and a weakening ADX would be a more compelling case for caution than any one of those readings in isolation.

Risk Management: Knowing When to Exit a Losing Trade

Technical indicators help identify potential entry and exit points, but they are most valuable when paired with disciplined risk management. One of the most common — and costly — mistakes made by retail traders is the reluctance to close losing positions.

Behavioral finance research consistently shows that the psychological pain of realizing a loss feels more acute than the pleasure of an equivalent gain — a phenomenon known as loss aversion. This bias can cause traders to hold losing positions far longer than is rational, hoping for a recovery that may never come.

Practical approaches to managing this risk include:

• Pre-defined stop-loss levels: Set exit points before entering a trade, based on technical levels rather than emotion.
• Position sizing: Limit the size of any single position so that a full loss is tolerable within the overall portfolio context.
• Regular portfolio review: Periodically assess every open position against its original thesis — if the thesis is invalidated, the position should be reconsidered regardless of unrealized profit or loss.

Technical indicators such as those described in this article can serve as objective, rules-based triggers for these decisions — helping traders act on evidence rather than hope.

Summary

The Mass Index, Williams %R, ADX, CCI, moving averages, and RSI each illuminate a different dimension of market behavior — volatility, momentum, trend strength, and mean reversion. Used together and interpreted in context, they form a more reliable basis for trading decisions than any single signal. The specific readings discussed here (sourced from August 2019 data) are illustrative only; always apply these tools to current data and in the context of your own risk tolerance and investment objectives.

AWS Told Partners Not to Say ‘Multi-Cloud’

In August 2019, Amazon Web Services released two partner-facing documents — an updated Co-Branding Guide and an APN Messaging and Branding Guide — that contained notably restrictive language about how AWS Partner Network (APN) members could describe cloud infrastructure in co-branded and general marketing materials.

The language in both documents was explicit:

“AWS does not allow or approve use of the terms ‘multi-cloud,’ ‘cross cloud,’ ‘any cloud,’ ‘every cloud’ or any other language that implies designing or supporting more than one cloud provider.”

AWS Co-Branding Guide (August 2019)

The APN Messaging and Branding Guide went further, extending the restriction to general marketing materials and adding that architecture diagrams or graphics depicting multiple cloud providers — whether identified by name, logo, or described generically — would also not receive AWS approval.

What Partners Were and Were Not Allowed to Say

The guidance created a detailed set of approved and prohibited terms. Key restrictions included:

• Banned terms: “multi-cloud”, “cross cloud”, “any cloud”, “every cloud”, “AWS hybrid”, “AWS hybrid cloud”, “AWS Partner”, “integrated” or “integrates” (when describing how a solution works with AWS services), and “runs in AWS”.
• Approved alternatives: “the cloud”, “your cloud”, “hybrid”, “hybrid architecture”, “hybrid cloud” (when not used to describe AWS itself), “runs on AWS”, “APN Partner”, and “member of the APN”.

The guide also prescribed how partners should frame hybrid architectures. Rather than positioning a solution as spanning multiple environments generically, partners were instructed to describe their offering as “extending or connecting an on-premises environment to AWS.”

Even the word “integrated” was flagged. The guide stated that using it implied a closer technical relationship with AWS than was appropriate, and partners were told to avoid it when describing how their solutions interact with AWS services.

One particularly granular rule concerned the preposition used with AWS: partners were told that something runs on AWS, not in AWS — because “in” implies deeper integration than “actually is.”

Google Cloud Responded Publicly — and Pointed to Anthos

Robert Enslin, who had become Google Cloud’s President of Global Customer Operations in April 2019, responded to CRN Australia’s coverage with a pointed tweet:

“Multi-cloud is a reality for many customers, and we embrace openness and choice. #Anthos”

Robert Enslin, Google Cloud (via Twitter, August 2019)

The hashtag was a deliberate reference. Google had unveiled Anthos at Google Cloud Next in April 2019 — the same month Enslin joined the company. Anthos is a hybrid and multi-cloud application management platform built on Kubernetes and Istio. Its core proposition is the ability to deploy and manage containerised workloads consistently across on-premises infrastructure, Google Cloud, and third-party clouds — explicitly including AWS and Microsoft Azure.

For Google Cloud, multi-cloud was not just a marketing term to be argued over — it was a product category that Anthos was built to serve. Enslin’s tweet framed AWS’s branding restrictions as being out of step with how enterprise customers were actually operating their infrastructure.

Why These Branding Rules Matter to Engineers and Architects

From a purely technical standpoint, it might be tempting to dismiss partner branding guidelines as a marketing concern. But the restrictions carried practical implications for DevOps teams and solutions architects working inside the AWS partner ecosystem:

• Architecture diagrams submitted for AWS co-marketing approval could not depict multi-cloud topologies — even for solutions that genuinely operated across clouds.
• ISVs and managed service providers building legitimately cross-cloud tooling faced a choice: sanitise their messaging to qualify for AWS joint marketing, or accurately represent their capabilities and forgo that channel.
• The “hybrid” carve-out had a catch. While the word “hybrid” was permitted in some contexts, it could not be used to characterise AWS itself — limiting how partners could describe AWS’s role in hybrid architectures that also included on-premises environments.

AWS’s Position and Its Silence

AWS did not respond to requests for comment reported by CRN Australia at the time. The company has historically emphasised the depth and breadth of its own platform rather than positioning around interoperability with competitors — a stance broadly consistent with these branding guidelines.

The restrictions did not prevent AWS partners from building multi-cloud solutions. They governed what could appear in co-branded and APN-approved marketing materials — a meaningful distinction, but one that still shaped how partners communicated their value propositions when working alongside AWS’s brand.

The Broader Context: Multi-Cloud as a Fault Line

The 2019 exchange was a visible moment in an ongoing strategic tension between the major cloud providers over how the industry defines and promotes multi-cloud adoption. Google Cloud and Microsoft Azure have both publicly embraced multi-cloud and hybrid messaging as differentiators — in part because AWS held a commanding market-share lead and had less commercial incentive to encourage workloads to spread across competitors.

For engineering teams evaluating platform strategy, the episode was a useful reminder that vendor marketing guidelines, partner programme terms, and product positioning can all reflect underlying competitive dynamics — and that the term “multi-cloud” itself had become contested commercial territory, not just a neutral infrastructure descriptor.

Original reporting: CRN Australia, August 2019.

Overview

Docker Enterprise 3.0 is a significant release that addresses enterprise pain points around Kubernetes complexity, cluster lifecycle management, application portability, and security. The release ships four headline capabilities: Docker Kubernetes Services (DKS), lifecycle automation tooling via a new CLI plugin, a virtual-machine-to-container conversion tool, and Docker Applications built on the Cloud Native Application Bundles (CNAB) specification.

Docker Kubernetes Services (DKS)

Docker Kubernetes Services is Docker’s answer to the operational complexity enterprises encounter when adopting Kubernetes at scale. The design goal is to provide intuitive security defaults and manageability configurations that typical enterprise teams expect — without requiring deep Kubernetes expertise from every stakeholder.

DKS integrates Kubernetes from the developer’s desktop all the way through to production servers. Key capabilities include:

• Version Packs: Keeps developer desktop environments synchronized with production Kubernetes versions, reducing environment drift and the classic “works on my machine” problem.
• Up-to-date Kubernetes: Ships the latest Kubernetes release available at GA time, including full support for the Container Storage Interface (CSI) — the standard plugin interface for storage drivers.
• Unified experience: The same DKS tooling is used by developers, operators, and security teams, lowering the organizational barrier to Kubernetes adoption.

Cluster Lifecycle Automation with the Docker Cluster CLI Plugin

Managing a Kubernetes cluster’s full lifecycle — provisioning, scaling, upgrading, backing up, and restoring — is a significant operational burden. Docker Enterprise 3.0 introduces a CLI plugin built on Docker Certified Infrastructure that wraps these operations behind a declarative, familiar Docker CLI experience.

Key Commands

Upgrades can be performed with zero downtime using a blue-green deployment model for worker nodes: new worker nodes running the target version are brought online before old nodes are drained and removed, ensuring continuous availability of workloads throughout the upgrade process.

VM-to-Container Conversion Tool

One of the more practically interesting additions in Docker Enterprise 3.0 is an automated conversion tool aimed at organizations still running applications on virtual machines or bare-metal servers. The tool works by scanning a running server to collect:

• Operating system configuration
• Web server settings
• Application runtime configuration

It then assembles this information into a Dockerfile and stages the application code in a local directory, ready for a docker build on a modern OS. A concrete example given by Docker: a Windows Server 2003 environment running IIS-based .NET applications across disparate IIS Application Pools can be scanned to automatically generate the corresponding Dockerfiles. This significantly lowers the barrier for lift-and-shift containerisation of legacy Windows workloads.

Docker Applications and Cloud Native Application Bundles (CNAB)

Docker Applications is a new toolset for building, sharing, and running multi-service applications across multiple configuration formats. The core idea is to bundle together application descriptions, component definitions, and runtime parameters into a single, portable unit — effectively a “container of containers.”

Docker Applications are an implementation of the Cloud Native Application Bundles (CNAB) specification, originally co-developed by Docker, Microsoft, HashiCorp, Bitnami, and Codefresh. A Docker Application can:

• Identify and pull required container images from Docker Hub
• Generate Docker Compose YAML files
• Generate Kubernetes YAML manifests
• Generate Helm charts
• Produce other required configuration artifacts

Application Templates

Docker Application Templates let developers bootstrap new applications from a library of predefined, organization-approved templates — without needing prior knowledge of Docker commands. Templates can include:

• Dockerfiles and custom base images
• Common Docker Compose service YAML snippets
• Standardised networking and volume configurations

This is particularly valuable for enterprises wanting to enforce best-practice patterns across development teams while accelerating onboarding.

Security Enhancements

Docker Enterprise 3.0 ships two notable security additions:

• Group Managed Service Accounts (gMSA) for Swarm: Extends Windows Active Directory gMSA support to Docker Swarm workloads, allowing containerised Windows services to authenticate to AD-integrated resources without embedding credentials in images or environment variables.
• PKI Certificate-Based Authentication: Adds certificate-based auth as an alternative to password-based access, improving security posture for API and CLI interactions with the platform.

Docker Desktop Enterprise vs. Docker Desktop Community

Docker Enterprise 3.0 also ships an updated Docker Desktop Enterprise edition. Docker Desktop Enterprise includes features not available in the Community edition — most notably Version Packs (for locking the desktop Kubernetes version to match production) and centrally managed configuration policies. The original source article referenced a comparison table between the two editions; refer to the official Docker Enterprise product page for the current, authoritative feature comparison, as the specific table was not reproduced in the source used for this article.

Summary

Docker Enterprise 3.0 positions the platform as a full-lifecycle Kubernetes management solution for enterprises, going well beyond basic container orchestration. The combination of DKS Version Packs, zero-downtime cluster upgrades, automated VM-to-container conversion, and CNAB-based application bundles represents a meaningful step toward reducing the operational overhead that has historically made enterprise Kubernetes adoption difficult. Security improvements like gMSA for Swarm and PKI authentication address compliance requirements that are non-negotiable in regulated industries.

A financial services company will retrain its help desk staff to be app developers and SREs, while AI handles the grunt work for the company’s call center employees.

Software-based automation of menial tasks will pave the way for more meaningful work among IT ops pros at a financial services company.

Until 2018, IT ops teams at Freedom Financial Network, a 17-year-old financial services company focused on consumer debt reduction, were largely focused on help desk tasks for the company’s 2,200 call center employees. The company had also made some progress in cloud migration and the adoption of Agile and DevOps practices, but wanted to take a more automated approach to its everyday IT tasks and rethink how it assigned its employees.

“Having people do application support and begin SRE training changes the game versus paying people $22 an hour to answer the phone and open ServiceNow tickets,” said Mark Tonnesen, CIO at Freedom Financial, headquartered in San Mateo, Calif.

Tonnesen said he briefly considered AI help desk features from ServiceNow and AIOps software from Moogsoft, but heard word-of-mouth recommendations from other CIOs about a stealth startup called Moveworks. The company, officially launched in April 2019, was founded by former Google employees that specialized in natural language processing at the web giant.

The SaaS-based Moveworks AI help desk software integrates with Slack, Microsoft Teams and other ChatOps services, along with email. It interacts with Freedom Financial’s call center employees through chatbots and email conversations, and processes their help desk requests through an AI-driven probabilistic decision engine.

“There are a lot of help desk automation tools on the market that do things like password resets,” Tonnesen said. “But they usually still require people on the back end.”

Since it implemented Moveworks in 2018, Freedom Financial has automated between 15% and 20% of its help desk tasks, and hopes to automate 25% to 30% by the end of 2019. At the same time, it will rework its IT ops teams and retrain them.

What Is a Software Container?

A software container is a lightweight, executable unit of software that packages an application together with everything it needs to run: libraries, configuration files, dependencies, and other binaries. The goal is to decouple an application from the underlying network infrastructure and operating system so that it behaves consistently regardless of where it runs — on a developer’s laptop, a staging server, or a production cloud environment.

Unlike traditional virtual machines (VMs), containers share the host operating system’s kernel rather than bundling a full OS of their own. This architecture makes containers significantly smaller and faster to start than VMs, and allows a single server to host many more container instances than equivalent VM workloads.

Containers vs. Virtualization: Key Differences

Understanding why containers have gained traction requires comparing them directly with hardware virtualization:

The trade-off is isolation: because containers share the host kernel, a kernel-level vulnerability can potentially affect all containers on that host. Hardware virtualization provides a stronger security boundary at the cost of greater resource consumption. This is an active area of innovation, with projects such as gVisor and Kata Containers working to close the security gap.

Core Use Cases Driving Adoption

Several application categories have emerged as primary drivers of enterprise container adoption:

• CI/CD pipelines: Containers provide consistent, reproducible build and test environments, reducing “works on my machine” problems and speeding up release cycles.
• Microservices architectures: Each service can be packaged, deployed, scaled, and updated independently, enabling teams to move faster without tight coupling.
• Monitoring and logging: Containerized agents can be deployed as sidecars or DaemonSets, giving operators uniform observability across heterogeneous workloads.
• Storage and data services: Stateful workloads increasingly run in containers using persistent volume abstractions provided by orchestration platforms.
• Networking and service mesh: Container-native networking tools manage traffic routing, load balancing, and inter-service communication at scale.
• Security tooling: Image scanning, runtime policy enforcement, and secrets management are a growing product category targeting containerized environments.
• Management and orchestration: Platforms such as Kubernetes automate container scheduling, self-healing, scaling, and rolling deployments.

Competitive Landscape and Key Players

The software container ecosystem spans runtime engines, orchestration platforms, registries, security tools, and managed cloud services. Notable participants at the time of the original market overview (2019) included:

• Docker Inc. — popularised application containers and the image format that became the basis of the OCI (Open Container Initiative) standard.
• CoreOS, Inc. — contributed rkt (an alternative container runtime) and the etcd distributed key-value store before being acquired by Red Hat in 2018.
• Codenvy, Inc. — offered cloud-based developer workspaces built on container technology (later acquired by Red Hat and folded into Eclipse Che).

The broader ecosystem also includes major cloud providers (AWS, Google Cloud, Microsoft Azure), platform vendors (Red Hat/IBM, VMware, SUSE), and a large number of open-source projects under the Cloud Native Computing Foundation (CNCF) umbrella.

Market Dynamics and Growth Factors

Several structural factors have sustained demand for container technology:

• Open-source momentum: The majority of foundational container tooling — Kubernetes, containerd, CRI-O, Prometheus, Envoy — is open source and vendor-neutral. Enterprise buyers have shown a clear preference for open standards over proprietary lock-in.
• Reduced licensing overhead: Sharing a single host OS kernel across many containers eliminates the per-VM operating system licensing cost that characterised earlier virtualisation strategies.
• Cloud-native application design: The shift toward microservices and twelve-factor applications naturally fits the container deployment model.
• Partnership ecosystem: System integrators, ISVs, and cloud providers have built managed container services (Amazon ECS/EKS, Google GKE, Azure AKS) that lower the operational burden for enterprise adopters.
• Startup and innovation activity: New entrants in security, observability, and developer experience continue to expand the ecosystem, often partnering with or being acquired by larger vendors.

Remaining Challenges

Despite strong growth, the container market still faces several unsolved problems that vendors are actively working on:

• Security: Container escape vulnerabilities, image supply-chain attacks, and misconfigured registries remain real risks. Runtime security tools (Falco, Aqua Security, Sysdig) have emerged to address detection and policy enforcement.
• Networking complexity: Multi-cluster and multi-cloud networking, service mesh overhead, and CNI plugin fragmentation add operational complexity.
• Stateful workloads: Running databases and other stateful services in containers requires careful storage configuration and is more complex than stateless deployments.
• Operational expertise: Kubernetes in particular has a steep learning curve. The shortage of skilled practitioners is a market constraint as well as an opportunity for training and managed-service providers.

Geographic Segmentation

Container adoption is global, but adoption patterns vary by region. North America, particularly the United States, has historically led in enterprise container deployments, driven by a mature cloud infrastructure and an active startup ecosystem. Europe follows, with growing investment in cloud-native platforms. Asia Pacific represents a fast-growing segment, with large technology markets in China, India, Japan, and South Korea increasing adoption. Latin America and Middle East & Africa are at earlier stages of adoption but show increasing interest from enterprises modernising legacy infrastructure.

Summary

Software containers have matured from a developer convenience into a foundational enterprise infrastructure technology. Their ability to package applications with all dependencies, run consistently across environments, and consume fewer resources than VMs underpins their widespread adoption. The market continues to evolve rapidly around orchestration (Kubernetes), security, observability, and managed cloud services. Organisations evaluating container adoption should weigh the operational complexity of orchestration platforms against the long-term benefits of consistent, portable, and resource-efficient application delivery.

What Is a Cloud Computing System?

A cloud computing system is a group of servers interconnected to function as a unified platform. This platform has two primary components:

• Processing Nodes — virtualized servers that handle computation workloads.
• Storage Area Networks (SANs) — physically separated, dedicated storage infrastructure where persistent data resides.

The term cloud itself refers to this clustered, virtualized model. Rather than tying a workload to one physical machine, resources are drawn from a shared pool, enabling greater flexibility and resilience.

What Does Cloud Hosting Mean?

Cloud hosting means that internet hosting services — such as virtual servers or web hosting — are produced and delivered by a cluster of virtualized servers working together as a system. Understanding this requires distinguishing two related concepts:

• Virtual Machine (VM) / Virtual Server — a virtualized computing instance running on top of a single physical server.
• Cloud Server — a virtualized computing instance running on top of a clustered computing system, drawing resources from multiple physical nodes.

Cloud hosting therefore means your web or application workload is served from a cloud computing system rather than a single physical machine. This distinction matters for resilience: if one physical node fails, the workload can continue on another node in the cluster.

Types of Cloud Services

Cloud deployments are typically categorized into three models, each suited to different operational requirements:

Public Cloud

Public clouds are open to the internet and used to deliver IT services to external users or customers. Resources are shared across multiple tenants, though logically isolated. Public clouds are cost-effective for variable workloads because you pay only for what you consume.

Private Cloud

Private clouds are isolated computing environments not accessible over the public internet. They are built for the internal workflows of a specific organization, providing greater control over security, compliance, and resource allocation.

Hybrid Cloud

Hybrid clouds connect private and public cloud environments, enabling workloads, data, and operations to move between them. Organizations use hybrid architectures to keep sensitive workloads on private infrastructure while leveraging the scalability of public clouds for demand spikes or less sensitive services.

Key Advantages of Cloud Infrastructure

Resource Scalability

One of the most significant benefits of cloud computing over standalone physical servers is on-demand scalability. A cloud server can receive additional processor cores, memory, or storage at any time — typically without downtime — because resources are drawn from the shared pool rather than physically installed into a single machine.

High Availability (HA)

High Availability is a capability that automatically restarts a cloud server if it stops delivering services due to an operating system failure or an outage on the underlying physical host. HA reduces unplanned downtime by ensuring that a failed instance is quickly restored, often without manual intervention. It is one of the foundational resilience features of cloud platforms.

Automated Fault Tolerance (AFT)

Automated Fault Tolerance goes a step further than High Availability. Where HA reduces outage duration by restarting a failed instance, AFT eliminates service outages by maintaining a live shadow (failover) instance that is continuously synchronized with the primary cloud server.

In the event of an OS failure or hardware outage, the failover instance takes over automatically, delivering zero downtime and preventing data loss. The distinction is important for applications with strict uptime SLAs:

• High Availability → minimizes downtime (seconds to minutes to recover).
• Automated Fault Tolerance → eliminates perceived downtime (instant, transparent failover).

Cloud Virtualization Technologies

Cloud providers build their infrastructure on top of virtualization platforms. VMware is one widely adopted example in the enterprise space, offering tools for compute, storage, and network virtualization. Other major hypervisors and virtualization stacks used across the industry include KVM (used by OpenStack and many Linux-based clouds), Microsoft Hyper-V, and Xen. The choice of underlying virtualization technology affects the feature set, performance characteristics, and management capabilities available to cloud users.

Summary

Cloud services describe computing and hosting delivered from a clustered, virtualized infrastructure over the internet. Key concepts to understand are:

• Cloud Server vs. Virtual Machine — cluster-backed vs. single-host virtualization.
• Public, Private, and Hybrid deployment models — chosen based on security, cost, and operational needs.
• Scalability — add resources on demand without hardware changes.
• High Availability — automatic restart after failure.
• Automated Fault Tolerance — zero-downtime failover via a live shadow instance.

Understanding these distinctions helps engineers and architects select the right cloud model and features for their availability and performance requirements.