xalt_webinar_platform engineering

Webinar Platform Engineering: AWS account setup with JSM

In our webinar "Platform Engineering - Build AWS Accounts in Just One Hour with JSM Cloud", our DevOps ambassadors Chris and Ivan, along with Atlassian platform expert Marcin from BSH, introduced the transformative approach of platform engineering and how it is revolutionizing cloud infrastructure management for development teams. In our conversation, we discussed the concept of platform engineering, including how to initiate the process of using platform engineering, what obstacles organizations may encounter and how to overcome them with self-service for developers. We also showed how Jira Service Management can be used as a self-service for developers to create AWS accounts in just one hour.

Understanding platform engineering

"Platform Engineering is a foundation of self-service APIs, tools, services, knowledge and support designed as a compelling internal product," said Ivan Ermilov during the webinar. This concept is at the heart of internal developer platforms (IDPs)aimed at streamlining operations and supporting development teams. By simplifying access to cloud resources, platform engineering promotes a more efficient and autonomous working environment.

Find out more about platform engineering in our article "What is Platform Engineering“.

The decisive advantages

One of the key takeaways from the webinar was the numerous benefits that platform engineering brings. Not only does it speed up the delivery of features, but it also significantly reduces manual tasks for developers. The discussion highlighted how teams gain independence, leading to a more agile and responsive IT infrastructure.

Overcoming traditional challenges

Traditional methods of managing cloud infrastructure often lead to project delays and security compliance issues. Ivan pointed out that "a common scenario I've personally encountered in my career is that deploying infrastructure requires a cascade of approvals. The whole process can take weeks. One specific example we encounter in our customer environment is that AWS account provisioning can take weeks to complete. One reason for this is usually that the infrastructure landscape is simply inefficient and not standardized." By using platform engineering, companies can overcome these hurdles and pave the way for a more streamlined and secure process.

Success story from the field: BSH's journey

Marcin Guz from BSH told the story of the company's transformation and illustrated the transition to automated cloud infrastructure management. The practical aspects of implementing platform engineering principles were highlighted, emphasizing how operational efficiency could be improved.

Technical insights: The self-service model

Ivan and Chris Becker discussed the implementation of a self-service model using Jira Service Management (JSM) and automation pipelines. This approach allows developers to manage cloud resources, including the creation of AWS accounts, in as little as an hour - a marked difference from the days or weeks it used to take.

Live demo: Quick AWS account creation

A highlight was the live demonstration by Chris Becker, who presented the optimized process for setting up AWS accounts. This real-time presentation served as a practical guide for the audience, illustrating the simplicity and efficiency of the self-service model.

A look into the future: The future of platform engineering

The webinar concluded with a look to the future. Ivan spoke about exciting future developments such as multi-cloud strategies and the integration of DevSecOps approaches, giving an indication of the ever-evolving landscape of platform engineering.

Watch our webinar on-demand

Want to learn about the possibilities of platform engineering and developer self-service? Watch our on-demand webinar to learn more about platform engineering, IDPs and developer self-service. In this informative session, you'll gain insights that will help you transform your cloud infrastructure management.

Jira Service Management News Atlassian High Velocity 2023

Jira Service Management News from Atlassian's High Velocity Event 2023

The Atlassian community recently met at the High Velocity event in Sydney. At the event, Atlassian leaders presented groundbreaking new features in Jira Service Management (JSM) and announced new collaborations. JSM customers gave insights into how they use the Atlassian platform in their business, underlining how Atlassian is revolutionizing service management. The overarching motto was: End bad service management.

In this article, we give you an overview of the most exciting news and new features in Jira Service Management.

The most important news & new features at a glance

New cooperation:

  • New cooperation with Airtrack enables comprehensive asset management in JSM

New features:

  • Integration of Compass in JSM combines dev and ops data for full transparency
  • Asset dashboard provides meaningful insights
  • Integration of DevSecOps tools helps to create transparency about security vulnerabilities
  • Integration of CI/CD tools supports seamless collaboration between Dev and Ops
  • Customer support template optimizes support processes
  • Single sign-on for customer accounts creates a seamless user experience
  • Service management templates make teams more autonomous and faster
  • Board View of tickets for optimized overview
  • Dark Mode for eye-friendly working
  • Virtual Agent answers questions with the help of artificial intelligence
  • Agent Co-Pilot creates summaries and optimizes communication

Further news:

  • New upper limit of 20,000 agents per instance on JSM
  • Increase in the upper limits of objects in the asset and configuration database to 3 million
  • Expansion of regional export for data residency: newest region in Canada

Transparency through a "single source of truth"

Integration of Atlassian's Compass and JSM

Compass is one of the latest additions to the Atlassian family. This is a software catalog designed to assist developers in answering queries such as: How do I find a particular micro service? Who owns it? How do I get help if something goes wrong? How do I know if it meets security and compliance requirements?

At the same time, Compass serves as a monitoring tool that supports DevOps teams in monitoring software components and reacting quickly if something gets out of hand.

Atlassian Compass Dashboard
Compass supports development teams, which are often globally distributed and work independently of each other, in creating full visibility of a service and thus facilitating collaboration.

Thanks to the integration in JSM, the IT team, which handles the operational side of a service such as incident and change management, has a full overview of a service and its dependent components. If there is a problem with one of the service components, for example, IT can react and only roll out the change once the problem has been resolved.

Compass integration in Jira Service Management
JSM gives the IT operations team complete visibility of related services, whether all components are intact or whether development is working on a problem.

By combining Compass and JSM, developers and the IT team have a view of the same data source, but with the information that is important for their respective jobs. This solves the major challenge of updating data from traditional CMDBs (Configuration Management Database) and expands the view to the developer perspective.

Comparison of traditional CMDB and modern CMDB with Jira Service Management
Traditional CMDBs do not provide the complete picture that the IT operations team needs. It also implies a significant amount of work required to ensure that it remains current. Modern CMDBs bring Dev and Ops relevant information together in one platform, creating holistic transparency.

Comprehensive Asset Management with Airtrack and new Asset Dashboard

With the announcement that Airtrack is now part of the Atlassian family, JSM users can now operate comprehensive asset management. Airtrack supports companies in merging and analyzing different data sources and ensures that the data is correct, up-to-date and complete. It provides over 30 out-of-the-box connections, enables data reconciliation (e.g., helps identify missing dependencies between services; discovers unmanaged machines) and processes data beyond IT (e.g., managing security, compliance, billing, forecasting, etc.).

The asset data is stored in a new Asset Dashboard in JSM that provides meaningful insights and supports IT teams in their decision-making processes. Various reports can be created in the dashboard.

The extensive asset data is also available in Atlassian Analytics . This means that they can also be combined with data from other Atlassian tools and third-party tools. JSM thus brings development, infrastructure & operations and business teams together on one platform and creates transparency across the entire company.

Atlassian Analytics Dashboard with combined data from different sources
In Atlassian Analytics, additional data can be combined with the asset data: e.g. actual operating costs from the AWS Cloud, budget information from Snowflake in comparison with the assets from the JSM database. This gives you an overview of service, costs and performance in one place.

Breaking down silos for more relaxed collaboration

For Developers and IT Operations

Collaboration between developers and IT teams can be challenging. While developers want to quickly deliver new services and added value, the IT team makes sure that these do not pose any risks to operations. New integration options for developer tools in Jira Service Management are designed to eliminate these friction points and ensure seamless collaboration.

The Integration of DevSecOps-Tools in Jira makes it possible to manage risks better. It makes all security vulnerabilities visible within a sprint. Automation rules can also be created to automatically create tasks in Jira when a security vulnerability is identified. This ensures that all risks are addressed before the service is rolled out.

Through the Integration of common CI/CD tools development teams can create change requests without having to leave the tools they use on a daily basis. The change request is automatically created in JSM and can therefore be accessed directly by the IT team.

Ultimately, the result is an integrated process from development and risk assessment through to the approval and implementation of changes. High-risk services can be fed back to the development team in the CI/CD tool for checking before they are implemented in the production system.

With the new Release Hub in Jira they also have an overview of the status of their services, and automatic notifications inform them when a service has been rolled out.

Integrated process for DevOps with Jira Service Management
Jira Service Management integrates developer tools, creating a seamless system for Dev and Ops.

For Customer Support and Development teams

A new JSM template for customer support provides a convenient overview of all customer-relevant data and processes.

It also includes a feature that supports the seamless escalation process and improves collaboration between development teams and support teams. Support staff can escalate customer issues directly in JSM, and the tickets are created directly as bugs in Jira Software. This also allows developers to quickly see what impact the bug they are working on is having on customers. At the same time, the support team has a central overview of all escalated tickets.

For Customer Support and Customers

For seamless communication and ticket creation, customers can be provided with a single sign-on (SSO) solution. Jira Service Management now enables a connection to a separate SSO provider such as Microsoft Azure AD, Google Cloud Identity, etc.

Quick and easy set-up of various Service Desks

New service management templates for different areas of the company ensure that teams can quickly and easily create their own Service Desk. They contain preconfigured request forms and workflows that can be used directly.

The customization options for the service management templates have also been improved and simplified. Furthermore, users can choose from several best-practice templates or create their own forms.

This allows teams to act more autonomously and quickly without having to involve a system admin for setup and changes.

Jira Service Management templates for different Service Desk set-ups
Fast set-up and simple handling of the service management templates.

Working more progressively with Atlassian's Artificial Intelligence & Co.

New features for a more user-friendly application

Under this motto, JSM now offers a highly-requested function: The view of tickets in a Board View simplifies the overview and offers the usual drag-and-drop options.

New Board View for handling tickets in Jira Service Management
Improved ticket overview and intuitive editing options.

Another new feature is that, for example, night-time users working on support tickets can now also use JSM in Dark Mode for a more eye-friendly experience.

Integrated Artificial Intelligence (AI) simplifies daily tasks and increases work efficiency

With the vision of freeing employees from repetitive tasks and scaling Service Desks, the Virtual Agent is now available in JSM. The Virtual Agent is able to ask a logical follow-up question to an employee question in order to play out the answer for the employee as concretely as possible.

Virtual Agent in Jira Service Management
The Virtual Agent quickly provides the right answer with the help of a predefined sequence of follow-up questions.

The unique advantage of the Virtual Agent is that it is designed in such a way that anyone can set it up themselves. This is made possible by an easy-to-use no-code interface in which the employee can determine the path that a request goes through. This means that the agent can be set up within a few hours instead of spending days and weeks.

No-Code-Interface of the Virtual Agent for easy handling
The Virtual Agent can also be installed within a few hours via a no-code interface by
non-technical employees.

The features of the Agent Co-Pilot (powered by Atlassian Intelligence) has been rolled out. This is intended to improve service management quality in particular, which often suffers when different support employees take turns working on a ticket. The challenge for employees is to keep up to date with the latest information each time, which can be very time-consuming.

With just one click, the Agent Co-Pilot provides a short and concise summary of all processes that have already been documented in this ticket and brings the support employee up to speed in the shortest possible time.

The agent also assists with the formulation of messages to make communication as efficient and clear as possible. It reformulates written texts so that they are clear and professional and provide the necessary context for the recipient.

More news about Jira Service Management

Further news at the High Velocity Event was that the upper limits were raised as follows:

  • for agents per JSM instance to 20,000 agents
  • and for objects in the asset and configuration database to 3 million.

Regional export for data residency has also been extended to the Canadian region. The following figure summarizes these updates once again.

Summary of the news in Jira Service Management

Atlassian's future vision for Service Management

Finally, Atlassian's vision for its service management platform was emphasized: No matter how many different technologies, teams, and systems are in use in the service area - Jira Service Management is connected to all systems as a central platform and serves as a control system to coordinate and solve requests, regardless of which system they are solved in.

Artificial intelligence helps to provide quick, clear and consistent answers. It is also connected to all systems, collects the information there and delivers it in a concise summary.

If you would like to watch the keynote and sessions from High Velocity in Sydney, you can find the video recordings here: https://events.atlassian.com/highvelocity/

What is Platform Engineering

What is Platform Engineering

IT teams, developers, department heads and CTOs must ensure that applications and digital products are launched quickly, efficiently and securely and are always available. But often the conditions for this are not given. Compliance and security policies, as well as long and complicated processes, make it difficult for IT teams to achieve these goals. But this doesn't have to be the case and can be solved with the help of a developer self-service or Internal Developer Platform.

Simplified comparison of Platform Engineering vs Internal Developer Platform vs Developer Self-Service.

Platform Engineering vs. Internal Developer Platform vs. Developer Self-Service

What is Platform Engineering?

Platform Engineering is a new trend that aims to modernize enterprise software delivery. Platform engineering implements reusable tools and self-service capabilities with automated infrastructure workflows that improve developer experience and productivity. Initial platform engineering efforts often start with internal developer platforms (IDPs).

Platform Engineering helps make software creation and delivery faster and easier by providing unified tools, workflows, and technical foundations. It's like a well-organized toolkit and workshop for software developers to get their work done more efficiently and without unnecessary obstacles.

Webinar - Platform Engineering: AWS Account Creation with Developer Self-Service (Jira Service Management)

What is Platform Engineering used for?

The ideal development platform for one company may be completely unusable for another. Even within the same company, different development teams may have very different requirements.

The main goal of a technology platform is to increase developer productivity. At the enterprise level, such platforms promote consistency and efficiency. For developers, they provide significant relief in dealing with delivery pipelines and low-level infrastructure.

What is an Internal Developer Platform (IDP)?

Internal Developer Platforms (IDPs), also known as Developer Self-Service Platforms, are systems set up within organizations to accelerate and simplify the software development process. They provide developers with a centralized, standardized, and automated environment in which to write, test, deploy, and manage code.

IDPs provide a set of tools, features, and processes. The goal is to provide developers with a smooth self-service experience that offers the right features to help developers and others produce valuable software with as little effort as possible.

How is Platform Engineering different from Internal Developer Platform?

Platform Engineering is the overarching area that deals with the creation and management of software platforms. Within Platform Engineering, Integrated Development Platforms (IDPs) are developed as specific tools or platforms. These offer developers self-service and automation functions.

What is Developer Self-Service?

Developer Self-Service is a concept that enables developers to create and manage the resources and environments they need themselves, without having to wait for support from operations teams or other departments. This increases efficiency, reduces wait times, and increases productivity through self-service and faster access to resources. This means developers don't have to wait for others to get what they need and can get their work done faster.

How do IDPs help with this?

Think of Internal Developer Platforms (IDPs) as a well-organized supermarket where everything is easy to find. IDPs provide all the tools and services necessary for developers to get their jobs done without much hassle. They are, so to speak, the place where self-service takes place.

The transition to platform engineering

When a company moves from IDPs to Platform Engineering, it's like making the leap from a small local store to a large purchasing center. Platform Engineering offers a broader range of services and greater automation. It helps companies further streamline and scale their development processes.

By moving to Platform Engineering, companies can make their development processes more efficient, improve collaboration, and ultimately bring better products to market faster. The first step with IDPs and Developer Self-Service lays the foundation to achieve this higher level of efficiency and automation.

Challenges that can be solved with platform engineering

Scalability & Standardization

In growing companies, as well as large and established ones, the number of IT projects and teams can grow rapidly. Traditional development practices can make it difficult to scale the development environment and keep everyone homogeneous. As IT projects or applications continue to grow, there are differences in setup and configuration, security and compliance standards, and an overview of which user has access to what.

Platform Engineering enables greater scalability by introducing automation and standardized processes that make it easier to handle a growing number of projects and application developments.

Efficiency and productivity

Delays in developing and building infrastructure can be caused by manual processes and dependencies between teams, increasing the time to market for applications. Platform Engineering helps overcome these challenges by providing self-service capabilities and automation that enable teams to work faster and more independently.

Security & Compliance

Security concerns are central to any development process. Through platform engineering, we standardize and integrate security and compliance standards into the development process and IT infrastructure in advance, enabling consistent security auditing and management.

Consistency and standardization

Different teams and projects might use different tools and practices, which can lead to inconsistencies. Platform engineering promotes standardization by providing a common platform with consistent tools and processes that can be used by everyone.

Innovation and experimentation

The ability to quickly test and iterate on new ideas is critical to a company's ability to innovate. Platform Engineering provides an environment that encourages experimentation and rapid iteration by efficiently providing the necessary infrastructure and tools.

Cost control

Optimizing and automating development processes can reduce operating costs. Platform Engineering provides the tools and practices to use resources efficiently and thus reduce the total cost of development.

Real-world example: IDP and Developer Self-Service with Jira Service Management and AWS

One way to start with platform engineering is for example Jira Service Management as a developer self-service to set up AWS cloud infrastructure in an automated and secure way and to provide templates for developers and cloud engineers in a wiki.

How does it work?

Developer self-service for automatic AWS account creation with Jira service management

Jira Service Management Developer Self-Service

Using Jira Service Management, one of our customers provides a self-service that allows developers to set up an AWS organization account automatically and securely. This works with a simple portal and a service request form where the user has to provide information like name, function, account type, security and technical responsible and approving manager.

The account is then created on AWS in the backend using Python scripts in a build pipeline. During setup, all security and compliance relevant standards are already integrated and the JSM self-service is linked to the company's Active Directory. Due to the deep integration with all relevant systems of the company, it is possible to explicitly track who has access to what. This also facilitates the control of accesses and existing accounts in retrospect.

The result: The time required to create AWS organization accounts is reduced to less than an hour (from several weeks) with the help of JSM, enabling IT teams to publish, test and update their products faster. It also provides visibility into which and how many accounts already exist and for which product, making it easier to control the cost of cloud infrastructure on AWS.

Confluence Cloud as a knowledge database for IT teams

Of course, developer self-service is only a small part of platform engineering. IT teams need concrete tools and apps tailored to their needs.

One of these tools is a knowledgebase where IT teams, from developers to cloud engineers, can find relevant information such as templates that make their work easier and faster.

We have built a knowledge database with Confluence at our customer that provides a wide variety of templates, courses, best practices, and important information about processes. This knowledge database enables all relevant stakeholders to obtain important information and further training at any time.

Webinar - The First Step in Platform Engineering with a Developer Self-Service and JSM

After discussing the challenges and solutions that Platform Engineering brings, it is important to put these concepts into practice and explore them further. A great opportunity to learn more about the practical application of Platform Engineering is an upcoming webinar. This webinar will put a special focus on automating AWS infrastructure creation using Jira Service Management and Developer Self-Service. In addition, ess will feature a live demo with our DevOps experts.

Webinar - Platform Engineering: AWS Account Creation with Developer Self-Service (Jira Service Management)


The journey from Internal Developer Platforms to Platform Engineering is a progressive step that helps organizations optimize their development processes. By leveraging a Developer Self-Service and overcoming software development challenges, Platform Engineering paves the way for more efficient and innovative development practices. With practical resources like the featured webinar, interested parties can dive deeper into this topic. And also gain valuable insights into how to effectively implement Platform Engineering.

A comparison of popular container orchestration tools: Kubernetes vs Amazon ECS vs Azure Container Apps

A comparison of popular container orchestration tools

With the increasing adoption of new technologies and the shift to cloud-native environments, container orchestration has become an indispensable tool for deploying, scaling and managing containerized applications. Kubernetes, Amazon ECS and Azure Container Apps have emerged as leaders among the many options available. But with so many options, how can you figure out which one is best for your business?

In this article, we'll take an in-depth look at the features and benefits of Kubernetes, Amazon ECS, and Azure Container Apps and compare them side-by-side so you can make an informed decision. We'll address real-world use cases and explore the pros and cons of each option so you can choose the tool that best meets your organization's needs. By the end of this article, you'll have a clear understanding of the benefits and limitations of each tool and be able to make a decision that aligns with your business goals.

Let's get started!

Overview: Container Orchestration Tools

Explanation of the common tools

While Kubernetes is the most widely used container orchestration tool, there are other options that should be considered. Some of the other popular options are:

  • Amazon ECS is a fully managed container orchestration service that simplifies the deployment, management, and scaling of Docker containers.
  • Azure Container Apps is a fully managed environment that allows you to run microservices and containerized apps on a serverless platform.
  • Kubernetes is an open source platform that automates the deployment, scaling and management of containerized applications.


Let's start with an overview of Kubernetes. Kubernetes was developed by Google and is now maintained by the Cloud Native Computing Foundation. Kubernetes is an open source platform that automates the deployment, scaling, and management of container applications. Its flexibility and scalability make it a popular choice for organizations of all sizes, from small startups to large enterprises.

Why is Kubernetes so popular?

Kubernetes is widely considered the industry standard for container orchestration, and for good reason. It offers a wide range of features that make it ideal for large-scale, production-scale deployment.

  • Automatic scaling: Kubernetes can automatically increase or decrease the number of replicas of a containerized application based on resource utilization.
  • Self-healing: Kubernetes can automatically replace or reschedule containers that fail.
  • Service discovery and load balancing: Kubernetes can automatically discover services and balance traffic between them.
  • Rollbacks and rollouts: With Kubernetes, you can easily revert to a previous version of your application or do a gradual rollout of updates.
  • High availability: Kubernetes can automatically schedule and manage application replica availability.

The Kubernetes ecosystem also includes Internet-of-Things (IoT) deployments. There are special Kubernetes distributions (e.g. k3s, kubeedge, microk8s) that allow Kubernetes to be installed on telecom devices, satellites, or even a Boston Dynamics robot dog.

The main advantages of Kubernetes

One of the key benefits of Kubernetes is its ability to manage many nodes and containers, making it particularly suitable for organizations with high scaling requirements. Many of the largest and most complex applications in production today, such as those from Google, Uber, and Shopify, are powered by Kubernetes.

Another great advantage of Kubernetes is its wide ecosystem of third-party extensions and tools. They easily integrate with other services such as monitoring and logging platforms, CI/CD pipelines, and others. This flexibility allows organizations to develop and manage their applications in the way that best suits their needs.

Disadvantages of Kubernetes

But Kubernetes is not without its drawbacks. One of the biggest criticisms of Kubernetes is that it can be complex to set up and manage, especially for smaller companies without dedicated DevOps teams. In addition, some users report that Kubernetes can be resource intensive, which can be a problem for organizations with limited resources.

So is Kubernetes the right choice for your business?

If you're looking for a highly scalable, flexible, and feature-rich platform with a large ecosystem of third-party extensions, Kubernetes may be the perfect choice. However, if you are a smaller organization with limited resources and little experience with container orchestration, you should consider other options.

Managed Kubernetes Services

Want to take advantage of the scalability and flexibility of Kubernetes, but don't have the resources or experience to handle the complexity? There are managed Kubernetes services like GKE, EKS and AKS that can help you overcome that.

Kubernetes offerings in the cloud significantly lower the barrier to entry for Kubernetes adoption because of lower installation and maintenance costs. However, this does not mean that there are no costs at all, as most offerings have a shared responsibility model. For example, upgrades to Kubernetes clusters are typically performed by the owner of a Kubernetes cluster, not the cloud provider. Version upgrades require planning and an appropriate testing framework for your applications to ensure a smooth transition.

Use cases

Kubernetes is used by many of the world's largest companies, including Google, Facebook and Uber. It is well suited for large-scale, production-ready deployments.

  • Google: Google uses Kubernetes to manage the delivery of its search and advertising services.
  • Netflix: Netflix uses Kubernetes to deploy and manage its microservices.
  • IBM: IBM uses Kubernetes to manage its cloud services.

Comparison with other orchestration tools

While Kubernetes is widely considered the industry standard for container orchestration, it may not be the best solution for every organization. For example, if you have a small deployment or a limited budget, you may be better off with a simpler tool like Amazon ECS or even a simple container engine installation. For large, production-ready deployments, however, Kubernetes is hard to beat.

Advantages and disadvantages of Kubernetes as a container orchestration tool

Highly scalable and flexibleCan be complex to set up and manage
Large ecosystem of third-party extensionsResource-intensive
Widespread use in production by large companiesSteep learning curve for smaller organizations without their own DevOps teams
Managed Kubernetes services available to manage complexity
Can be installed on IoT devices

Amazon ECS: A powerful and scalable container management service

Amazon Elastic Container Service (ECS) is a highly scalable, high-performance container management service provided by Amazon Web Services (AWS). It allows you to run and manage Docker applications on a cluster of Amazon EC2 instances and provides a variety of features to help you optimize your container-based applications.

Features and Benefits Amazon ECS is characterized by a rich set of features and tight integration with other AWS services. It works hand-in-hand with the AWS CLI and Management Console, making it easy to launch, scale, and monitor your containerized applications.

ECS is fully managed by AWS, so you don't have to worry about managing the underlying infrastructure. It builds on the robustness of AWS and is compatible with a wide range of AWS tools and services.

Why is Amazon ECS so popular?

Amazon ECS is popular for a number of reasons, making it suitable for a variety of deployment scenarios:

  • Powerful and easy to use: Amazon ECS integrates well with the AWS CLI and AWS Management Console and provides a seamless experience for developers already using AWS.
  • Scalability: ECS is designed to easily handle large, enterprise-wide deployments and automatically scales to meet the needs of your application.
  • High availability: ECS ensures high availability by enabling deployment in multiple regions, providing redundancy, and maintaining application availability.
  • Cost-effective: With ECS, you only pay for the AWS resources you use (e.g. EC2 instances, EBS volumes) and there are no additional upfront or licensing costs.

Use cases

Amazon ECS is suitable for large deployments and for enterprises looking for a fully managed container orchestration service.

  • Large-scale deployment: Due to its high scalability, ECS is an excellent choice for large-scale deployment of containerized applications.
  • Fully managed service: For organizations that do not want to manage their infrastructure themselves, ECS offers a fully managed service where the underlying servers and their configuration are managed by AWS.

Azure Container Apps: A managed and serverless container service

Azure Container Apps is a serverless container service provided by Microsoft Azure. It allows you to easily build, deploy, and scale containerized apps without having to worry about the underlying infrastructure.

Features and benefits Azure Container Apps offers simplicity and integration with Azure services. The intuitive user interface and good integration with the Azure CLI simplify the management of your containerized apps.

With Azure Container Apps, the infrastructure is fully managed by Microsoft Azure. It is also based on Azure's robust architecture, which ensures seamless interoperability with other Azure services.

Why is Azure Container Apps so popular?

Azure Container Apps offers a number of benefits that are suitable for a wide range of deployments:

  • Ease of use: Azure Container Apps is integrated with the Azure CLI and Azure Portal, providing a familiar interface for developers already using Azure.
  • Serverless: Azure Container Apps abstracts the underlying infrastructure, giving developers more freedom to focus on programming and less on operations.
  • Highly scalable: Azure Container Apps can scale automatically to meet the needs of your application, making it well suited for applications with fluctuating demand.
  • Cost-effective: Azure Container Apps is only charged for the resources you use, and there are no additional infrastructure or licensing costs.

Use cases

Azure Container Apps is great for applications that require scalability and a serverless deployment model.

  • Scalable applications: Because Azure Container Apps automatically scales, it is ideal for applications that need to handle variable workloads.
  • Serverless model: Azure Container Apps offers a serverless deployment model for organizations that prefer not to manage servers and want to focus more on application development.

Amazon ECS vs. Azure CA vs. Kubernetes

Both Amazon ECS and Azure Container Apps are strong contenders in the container orchestration tool space. They offer robust, fully managed services that abstract the underlying infrastructure so developers can focus on their application code. However, they also cater to specific needs and ecosystems.

Amazon ECS is deeply integrated into the AWS ecosystem and is designed to easily handle large, enterprise-scale deployments. Azure Container Apps, on the other hand, operates on a serverless model and offers excellent scalability features, making it well suited for applications with fluctuating demand.

Here is a table for comparison to illustrate these points:

Amazon ECSAzure Container AppsKubernetes
Ecosystem compatibilityDeep integration with AWS servicesDeep integration with Azure servicesWidely compatible with many cloud providers
Deployment modelManaged service on EC2 instancesServerlessSelf-managed and hosted options available
ScalabilityDesigned for large-scale implementationsExcellent for variable demand (automatic scaling)Highly scalable with manual configuration
ManagementFully managed by AWSFully managed by Microsoft AzureManual, with complexity
CostsPayment for AWS resources usedPay for resources used, serverless modelDepends on hosting environment, can be cost-effective if self-managed
High availabilitytCross-regional deployments for high availabilityManaged high availabilityManual setup required for high availability

When choosing the right container orchestration tool for your organization, it's important to carefully evaluate your specific needs and compare them to the features and benefits of each tool.

Are you looking for a tool that can handle different workloads? Or are you looking for a simple and flexible tool that is easy to manage? Or are you looking for a tool that focuses on multi-cluster management and security?

Check out these options and see which one best fits your needs.


In this article, we've explored the features and benefits of Kubernetes, Amazon ECS, Azure Containers, and other popular container orchestration tools and compared them side-by-side to help you make an informed decision. We also examined real-world use cases and reviewed the pros and cons of each option, found that Kubernetes is widely considered the industry standard for container orchestration and is well suited for large-scale, production-ready deployments. We also saw that each container orchestration tool has its pros and cons.

Monitoring and Observability for DevOps Teams

Deep Dive: Monitoring and Observability for DevOps Teams

Concepts, Best Practices and Tools

DevOps teams are under constant pressure to deliver high-quality software quickly. However, as systems become more complex and decentralized, it becomes increasingly difficult for teams to understand the behavior of their systems and to detect and diagnose problems. This is where monitoring and observability come into play. But what exactly are monitoring and observability, and why are they so important for DevOps teams?

Monitoring is the process of collecting and analyzing data about a system's performance and behavior. This allows teams to understand how their systems are performing in real time and quickly identify and diagnose problems.

Observability, on the other hand, is the ability to infer the internal state of a system from its external outputs. It provides deeper insights into the behavior of systems and helps teams understand how their systems behave under different conditions.

But why are monitoring and observability so important for DevOps teams?

The short answer is that they help teams release software faster and with fewer bugs. By providing real-time insight into the performance and behavior of systems, monitoring and observability help teams identify and diagnose problems early, before they become critical. Essentially, Monitoring and Observability provide rapid feedback on the state of the system at a given point in time. This allows teams to roll out new features with high confidence, resolve issues quickly, and avoid downtime, resulting in faster software delivery and higher customer satisfaction overall.

But how can DevOps teams effectively implement monitoring and observability? And what are the best tools for the job? Let's find out.

What is monitoring?

Monitoring is the foundation of Observability and the process of collecting, analyzing, and visualizing data about a system's performance and behavior. It enables teams to understand how their systems are performing in real time and to quickly identify and diagnose problems. There are different types of monitoring, each with its own tools and best practices.

What you can monitor

Application performance monitoring (APM)

APM is the monitoring of software application performance and availability. It is important for identifying bottlenecks and ensuring an optimal user experience. Teams use APM to get real-time visibility into the health of their applications, identify problems in specific application components, and optimize the user experience. Tools such as New Relic, AppDynamics, and Splunk are commonly used for APM.

Monitoring of system availability (uptime)

Monitoring system availability is important to ensure that IT services are available and performing around the clock. In today's digital world, downtime can result in significant financial loss and reputational damage. With system availability monitoring, teams can track the availability of servers, networks, and storage devices, detect outages or performance degradation, and quickly take countermeasures. Infrastructure monitoring tools such as Nagios, Zabbix and Datadog are widely used for this purpose.

Monitoring of complex system logs and metrics

With the advent of decentralized systems and containerization, such as Kubernetes, monitoring system logs and metrics has become even more important. It helps teams understand system behavior over time, identify patterns, and detect potential problems before they escalate. By monitoring logs and metrics, teams can ensure the health and stability of their Kubernetes clusters, diagnose problems immediately and improve resource allocation decisions. Tools such as Elasticsearch, Logstash, Kibana, and New Relic are commonly used to monitor complex logs and metrics.

How does monitoring help teams identify and diagnose problems?

How do I find the most interesting use case in my company to start implementing a monitoring solution? The answer is: it depends on the needs of your team and your specific use case. It's a good idea to first identify the most critical areas of your systems and then choose a monitoring strategy that best fits your needs.

With a good monitoring strategy, you can quickly detect and diagnose problems to avoid downtime and keep your customers happy. But monitoring is not the only solution. You also need to have visibility into the internal health of your systems; that's where observability comes in. The next section is about observability and how it complements monitoring.

What is Observability?

While monitoring provides real-time insight into the performance and behavior of systems, it does not give teams a complete view of how their systems behave under different conditions. This is where observability comes in.

Observability is the ability to infer the internal state of a system from its external outputs. It provides deeper insights into the behavior of systems and helps teams understand how their systems behave under different conditions.

The key to observability is understanding the three pillars of observability: metrics, traces, and logs.

The three pillars of observability: metrics, traces and logs

Metrics are quantitative measurements of the performance and behavior of a system. These include things like CPU utilization, memory usage, and request latency.

Traces are a set of events that describe a request as it flows through the system. They contain information about the path a request takes, the services it interacts with, and the time it spends at each service.

Logs are records of events that have occurred in a system. They contain information about errors, warnings and other types of events.

How Observability helps teams understand the behavior of their systems

By collecting and analyzing data from all three pillars of Observability, teams can gain a more comprehensive understanding of the behavior of their systems.

For example, if an application is running slowly, metrics can provide insight into how much CPU and memory is being consumed, traces can provide insight into which requests are taking the longest, and logs can reveal why requests are taking so long.

By combining data from all three pillars, teams can quickly identify the root cause of the problem and take action to fix it.

However, collecting and analyzing data from all three pillars of observability can be challenging.

How can DevOps teams effectively implement observability?

The answer is to use observability tools to take a comprehensive look at your systems. Tools like Grafana can collect and visualize data from all three pillars of observability, allowing teams to understand the behavior of their systems at a glance.

When you implement observability, you can understand the internal health of your systems. This allows you to fix problems before they become critical and identify patterns and trends that can lead to better performance, reliability and customer satisfaction.

The next section shows you how to implement monitoring and observability in your DevOps team.

How to implement monitoring and observability in DevOps?

  1. Discuss best practices for implementing monitoring and observability in a DevOps context.
  2. Explain how you use monitoring and observability tools effectively
  3. Describe how you can integrate monitoring and observability into the development process.

Now that we understand the importance of monitoring and observability and what they mean, let's discuss how to implement them in a DevOps context. Effective implementation of monitoring and observability requires a combination of the right tools, best practices, and a clear understanding of your team's needs and use cases.

Best practices for implementing monitoring and observability in a DevOps context.

In the DevOps context, monitoring and observability should be implemented strategically, focusing on customer impact and alignment with business goals. Monitoring systems should adhere to Service Level Agreements (SLAs), formal documents that guarantee a certain level of service, e.g. 99.5% uptime, and promise the customer compensation if these standards are not met.

Effective monitoring not only ensures that SLAs are met, but also protects the company's reputation and customer relationships. Poor reliability can damage trust and reputation. That's why proactive monitoring that includes continuous data collection, real-time analytics and rapid problem resolution is critical. Improved monitoring capabilities can be achieved with automated alerts, comprehensive logging, and end-to-end visibility tools.

As one of our experts at XALT says, "The best way to implement monitoring/observability is to support the business needs of the organization: achieving service level agreements (SLAs) for customers."

Another best practice for implementing monitoring and observability is to use monitoring and observability tools that provide a comprehensive view of your systems. As mentioned earlier, tools like Prometheus, Zipkin, Grafana, New Relic, and Coralgix can collect and visualize data from all three pillars of observability so teams can understand the behavior of their systems at a glance.

How to improve your implementation of monitoring and observability

An important aspect of monitoring and observability is its integration into the development process. As part of your build and deployment process, you can, for example, monitor your Continuous Integration and Delivery Pipeline to automatically collect and send data to your monitoring and observability tools. This way, monitoring and observability data is automatically collected and analyzed in real time, allowing teams to quickly identify and diagnose problems.

Establishing a clear process for incident management is another way to improve monitoring and observability implementation. When a problem occurs, your team will know exactly who is responsible and what actions need to be taken to resolve the issue. This is important because it ensures that the incident is resolved quickly and effectively, helping to minimize downtime and increase customer satisfaction.

You may be wondering, what's the best way to introduce Monitoring and Observability to my team?

The answer is that it depends on the needs of your team and your specific use case. The most important thing is to first identify the critical areas of your systems and then decide on a monitoring and observability strategy that best fits your needs.

By introducing monitoring and observability to your DevOps team, you can deliver software faster and with fewer bugs, improve the performance and reliability of your systems, and increase customer satisfaction.

Let's take a look at the best tools for monitoring and observability in the next section.

The Best Monitoring and Observability Tools for DevOps Teams

In the previous sections, we discussed the importance of monitoring and observability and how they can be implemented in the DevOps context.

But what are the best tools for the job?

In this section, we'll introduce some popular tools for monitoring and observability and explain how to choose the right tool for your team and use case.

There are a variety of tools for monitoring and observability. The most popular tools include Prometheus, Grafana, Elasticsearch, Logstash and Kibana (ELK).

  • Prometheus is an open source monitoring and observability tool widely used in the Kubernetes ecosystem. It provides a powerful query language and a variety of visualization options. It also integrates easily with other tools and services.
  • Grafana is an open source monitoring and observability tool that allows you to query and visualize data from various sources, including Prometheus. It offers a wide range of visualization options and is widely used in the Kubernetes ecosystem.
  • Kibana (ELK) is a set of open source tools for log management. Kibana is also a visualization tool that lets you create and share interactive dashboards based on data stored in Elasticsearch.
  • Elasticsearch is a powerful search engine used to index, search, and analyze logs. Logstash is a log collection and processing tool that can be used to collect, parse, and send logs to Elasticsearch.
  • OpenTelemetry is an open source project that provides a common set of APIs and libraries for telemetry. It is a common set of APIs for metrics and tracing. You can use it to instrument your applications and choose between different backends, including Prometheus, Jaeger, and Zipkin.
  • New Relic is a software analytics company that provides tools for real-time monitoring and performance analysis of software, infrastructure and customer experience.

How to choose the right tools for monitoring and observability

When choosing a monitoring and observability tool, it's important to consider the needs of your team and the use case. For example, if you are running a Kubernetes cluster, Prometheus and Grafana are good choices. If you need to manage a large number of logs, ELK might be a better choice. And if you're looking for a set of standard APIs for metrics and tracing, OpenTelemetry is a good choice.

It is not always necessary to choose just one tool. You can always use multiple monitoring and observability tools to cover different use cases. For example, you can use Prometheus for metrics, Zipkin for tracing, and ELK for log management.

By choosing the right tool for your team and use case, you can effectively leverage monitoring and observability to gain deeper insights into the behavior of your systems.


In this article we have taken a deep dive into the world of monitoring and observability for As a DevOps-teams. We discussed the importance of monitoring and observability, explained the concepts and practices in detail, and showed you how to implement monitoring and observability in your team. We also introduced some popular tools for monitoring and observability and explained how to choose the right tool for your team and use case.

In summary, monitoring is the collection and analysis of data about the performance and behavior of a system. Observability is the ability to infer the internal state of a system from its external outputs. Monitoring and observability are essential for DevOps teams to deliver software faster and with fewer bugs, improve system performance and reliability, and increase customer satisfaction. By using the right tools and best practices and integrating monitoring and observability into the development process, DevOps teams can gain real-time insights into the performance and behavior of their systems and quickly identify and diagnose problems.

Atlassian product news Team23

Atlassian product news Team23

Atlassians' recent Team23 event in Las Vegas was an impressive showcase of innovative products, integrations and powerful updates aimed at redefining the future of the workplace. In this article, you'll learn which Atlassian product innovations were unveiled at Team23. Among them Atlassian Intelligence, Confluence Whiteboards, Databases, Atlassian Together, Atlassian Analytics, Beacon, BYOK, Jira Product Discovery, OpenDevOps and Compass. 

From revolutionizing data management to improving team collaboration with artificial intelligence. Read on to learn more about the potential of these new solutions and how they can benefit your teams.

Atlassian product news

Atlassian Intelligence - AI in Confluence and Jira

AI is on the rise and is already being used in numerous products. It was only a matter of time before Atlassian introduced it into Jira and Confluence.

What is Atlassian Intelligence?

No matter whether you work with ConfluenceJira Software or Jira Service Management, Atlassian Intelligence helps you with your daily tasks: Meeting summaries, defining new tasks, and even writing responses.

Source: Atlassian

One of the main benefits of Atlassian Intelligence is that it provides institutional knowledge for Atlassian Cloud products, so users don't have to wonder what certain terms or concepts mean. Atlassian AI finds them if someone has already explained them in your Confluence knowledge base.

Another interesting feature of Atlassian Intelligence is that it understands natural language queries and provides instant answers. As a user, you can ask questions just like you would ask a teammate, and the AI will respond with helpful information. Basically, ChatGPT for your Confluence.

Atlassian Intelligence also lets you perform queries, or searches, like a human. It converts queries from natural language to JQL or SQL, which makes it easier to work with Jira Cloud products.

Atlassian Intelligence also provides virtual agents for Jira Service Management that are available 24/7 on Slack and Microsoft Teams to help employees immediately, at any time. This means less waiting and less work for users who need help quickly.

Learn more here: https://www.atlassian.com/software/artificial-intelligence

Confluence whiteboards

Everyone loves whiteboarding to gather ideas and collaborate with colleagues on new ideas or pressing issues. Until now, we had to resort to a physical whiteboard or digital solutions like Miro use

Atlassian product news: Confluence Whiteboards
Source: Atlassian

But now this (and more) is possible with Confluence.

Just like Miro, you can collaborate in real time, illustrate your ideas with stickies, lines, sections, and more, and share stamps, polls, and timers with your teammates.

With Whiteboard in Confluence, you can now turn your ideas into actions. This is done through the deep integration of Whiteboard functionality in Jira and Confluence.

Let's say you found a new set of tasks in your brainstorming session that you want to work on and track. With Confluence Whiteboards, you can now turn your stickies into Jira essues and/or Confluence pages, link Jira essues together to schedule tasks, and edit Jira essues and Confluence pages without leaving your whiteboard.

Learn more here: https://www.atlassian.com/software/confluence/whiteboards

Confluence databases

Who doesn't love to manage all their work in one place? With Confluence Databases, it's not just a dream, it's a reality.

Work aspects like Jira tasks, Confluence pages, due dates, statuses and much more are under your control in one place. It's the perfect solution to stay on top of all your work and make sure it's organized and under control.

What makes Confluence databases special is the live synchronization feature between databases and pages. Imagine always having the most up-to-date information at your fingertips, without tedious manual updates. A time-saver that guarantees you're always up to date.

Confluence Databases gives you the possibility to display your databases as tables, maps or boards. In addition, you can create a personalized view. Simply filter and sort the entries as you like. This makes your data easier to interpret and keeps you informed and up to date.

If efficiency and practicality are your top priorities, Confluence Databases is the right solution for you. User-friendly, powerful, and packed with features that help you get more done in less time, it's time to try Confluence Databases.

Sign up for a trial subscription: https://www.atlassian.com/software/confluence/databases

Atlassian Together

If you're looking for a tool that improves team collaboration and workflows across your organization, Atlassian Together could be just what you need. This powerful platform is designed to increase productivity by supporting team-oriented workflows and enabling seamless collaboration between decentralized teams, business and software units. Atlassian Together supports a flexible and efficient work environment that ensures remote working is as effective as on-site working.

Atlassian product news: Atlassian Together
Source Atlassian

One of the coolest features of Atlassian Together is its support for flexible task management and cross-team collaboration at scale.

This means you can combine structured and flexible working methods to create the best system for your team. What's more, Atlassian tools connect business and software teams to support alignment from development to launch.

Another benefit of Atlassian Together is corporate-grade security. The platform continuously tracks high-risk activities to monitor potential threats to the organization and ensure your team's data is always protected.

Atlassian Together includes a number of useful tools, such as Confluence, Jira Work Management, and Atlas. These tools help with task management, project management, and clear communication, making it easier for your team to collaborate effectively so they can get more done.

Learn more here: https://www.atlassian.com/solutions/work-management/together

Atlassian Analytics

Are you looking for a tool to visualize data from multiple sources and gain insights into your team's performance? Then Atlassian Analytics might be just what you need. With this powerful tool, you can create comprehensive visualizations of data from various sources (e.g. Excel, Google Sheets), including Atlassian products.

Atlassian product news: Atlassian Analytics
Source Atlassian

Atlassian Analytics includes preset templates for service management, asset management, content management, and DevOps use cases, making it easy for users to get started. It also provides a powerful SQL visual interface for custom data analytics and multiple data visualization options so you can create the perfect visualization for your needs.

Atlassian Analytics supports database links to query non-Atlassian data sources such as Snowflake, Amazon Redshift, Google BigQuery, Microsoft SQL Server, PostgreSQL, and others. This means you can use data from a wide range of sources to create comprehensive visualizations.

Atlassian Analytics also provides collaboration features that allow users to embed and comment on diagrams and manage permissions at the diagram level. This ensures that your team can collaborate effectively and make data-driven decisions.

Atlassian Analytics leverages data from multiple Atlassian products to accelerate decision making across DevOps, IT service management and business teams. The service connects seamlessly with the Atlassian Data Lake for data sources, allowing users to configure which products and instances to pull data from.

Learn more here: https://www.atlassian.com/platform/analytics/what-is-atlassian-analytics


Atlassian has unveiled Beacon, a software solution to detect, investigate and respond to risky activity in its cloud products. Beacon uses automated alerts, comprehensive investigation tools and response mechanisms to protect organizations from threats such as unauthorized data leakage, unauthorized access and insecure configurations.

Atlassian product news: Atlassian Beacon
Source: Atlassian

Key features:

  • Automatic alerts for unusual activity in Jira, Confluence and the Atlassian Admin Hub.
  • Detailed risk assessment capabilities, including user location, past alerts, and recent activity.
  • Optimized threat management through alert detail information, status tracking and SIEM forwarding.
  • Integration with Teams, Slack and SIEM to route alerts directly to the appropriate teams.
  • Protect against unauthorized information leakage with alerts on bulk exports, audit log exports, and external synchronization risks.
  • Identify high-risk user behavior at scale, with alerts for suspicious searches, unusual logins, policy changes and more.
  • App access monitoring with alerts for app installations and unsafe configurations.

Beacon by Atlassian provides a comprehensive enterprise security solution that enables instant detection, investigation and response to potential threats across Atlassian cloud products.

Learn more here: https://www.atlassian.com/software/beacon

BYOK - Bring-your-own-key encryption

Atlassian's cloud products already have world-class security measures in place, and customer data is protected at all times by Atlassian-managed keys in the WS Key Management Service (KMS).

As part of Team23, Bring your own key (BYOK) encryption was announced. An upgrade that gives you the ability to encode your Atlassian Cloud product data with keys that are securely stored in your own AWS account. This means you can manage your keys and revoke access whenever you see fit, whether for your end users or for Atlassian systems.

The advantages of BYOK:

  • Less risk: BYOK is like an additional security lock for your sensitive data, giving you an extra layer of protection.
  • Improved data management: Because your encoding keys are hosted in your AWS account, you can log and monitor access through AWS CloudTrail.
  • Increased control: Say goodbye to vendor dependency when it comes to blocking access. With BYOK, you're in charge.

Learn more about the release date and pricing here: https://www.atlassian.com/trust/privacy/byok

Jira Product Discovery

As a product manager, Jira Product Discovery is an invaluable tool that helps you organize, prioritize, and communicate your product ideas and insights.

Atlassian product news: Jira Product Discovery
Source: Atlassian

Imagine having all your ideas, user feedback, and product opportunities from different sources collected in one place and being able to evaluate them effectively.

With this tool, you can say goodbye to shared spreadsheets and presentations. Instead, enjoy the simplicity of custom lists and views to prioritize ideas based on impact, effort, and targeting.

Another outstanding feature of Jira Product Discovery is that it promotes seamless team collaboration. You'll appreciate the clear communication facilitated by custom roadmaps and views that change the way you think about product roadmaps.

And thanks to seamless integration with Jira software, you'll be well-informed from development to delivery and can link your product roadmaps and ideas to Epics for a holistic view.

Essentially, Jira Product Discovery combines the features of spreadsheets and PowerPoint into one easy-to-use tool that saves you from using third-party integration tools. It's your all-in-one solution for efficient product management.

Learn more here and get in for free: https://www.atlassian.com/software/jira/product-discovery


Compass provides a unified platform for developers to simplify and optimize their work on distributed software architectures. Imagine having a single platform where you can monitor your technical architecture through the catalog feature and apply technical best practices at scale using DevOps Health. This supports your team's autonomy and ensures that the components you work on are secure and reliable.

Atlassian product news: DevOps Compass
Source: Atlassian

Compass improves the user experience for development teams through its extensibility engine that connects information across your entire development toolchain. With real-time updates on component activity and dependencies, you get a consolidated view across development tools. Compass also provides an overarching view of all the components your team is working on, showing their dependencies and responsibilities.

The platform's extensive integration capabilities give you options for customizing components, teams, or global systems. Compass is more than just a tool; it is your partner in creating secure, compliant and efficient software architectures.

Learn more here: https://www.atlassian.com/software/compass

Build-Test-Deploy (CI/CD) pipeline

Advanced techniques for optimizing the CI/CD pipeline

Are you ready to revolutionize the way you build and deploy software? Welcome to the world of DevOps, where development and operations teams work seamlessly together to accelerate software delivery, increase reliability, and minimize risk. By adopting DevOps, you'll join a growing number of organizations that have already reaped the benefits of faster time to market, higher customer satisfaction, and increased overall efficiency. Learn advanced techniques to optimize your build-test-deploy (CI/CD) pipeline now.

I. Introduction: Unleash the Full Potential of Your Build-Test-Deploy (CI/CD) Pipeline

Unleashing the Power of DevOps

But what is the secret of a successful DevOps Transformation? It lies in optimizing your build-test-deploy pipeline. When your pipeline runs like a well-oiled machine, you have a smoother, more efficient process from code change to production deployment. So how can you optimize your pipeline to achieve unparalleled performance? It's time to learn the advanced techniques you can use to take your pipeline to the next level.

In this article, we'll introduce you to the advanced techniques you can use to optimize your build-test-deploy pipeline. We'll look at optimizing builds, tests, and deployments, as well as the critical importance of monitoring and feedback. By the end, you'll be equipped with the knowledge and tools you need to maximize the efficiency of your pipeline, stay ahead of the competition, and delight your customers with every release.

Are you ready to optimize your build-test-deploy (CI/CD) pipeline? Then let's get started.

II. build optimization techniques: Turbocharging your build process

A. Incremental Builds: Accelerate Development Without Compromise

Are you waiting for builds to complete and wasting valuable time that could be better spent developing features or fixing bugs? Incremental builds are the answer to speeding up your build process. By rebuilding only the parts of your code that have changed, you save valuable time and resources without compromising quality.

Benefit from the advantages of incremental builds

  • Faster build times
  • Reduced resource consumption
  • Improved developer productivity

Implementing Incremental Builds: A Strategic Approach

  • Choose a build system that supports incremental builds (e.g. Gradle, Bazel)
  • Organize your codebase into smaller, modular components
  • Use caching mechanisms to cache build artifacts

B. Dependency management: keep your codebase lean and secure

Have you ever struggled with a dependency conflict or vulnerability in your codebase? Proper dependency management is critical to avoiding such pitfalls and ensuring a healthy, efficient build process.

Popular dependency management tools: your trusted sidekicks

  • Maven for Java
  • Gradle for multilingual projects
  • npm for JavaScript

Strategies for maintaining healthy dependencies

  • Review and update dependencies regularly to minimize security risks
  • Use semantic versioning to ensure compatibility
  • Use of tools such as Dependabot to automate updates and vulnerability scans

C. Automating and parallelizing development: Unleashing unmatched efficiency

Are you still triggering builds manually and struggling with long build times? Build automation and parallelization will revolutionize your pipeline, streamline processes and shorten build times.

Continuous Integration (CI) tools: The backbone of build automation

  • Github with Github Actions: The most popular source code management and CI/CD tool on the market
  • Jenkins: The open source veteran
  • GitLab CI: Integrated CI/CD for GitLab users
  • CircleCI: A cloud-based powerhouse

Parallelize builds: Divide and conquer

  • Use the built-in parallelization features of your CI tool
  • Distribute tasks among multiple build agents
  • Use build tools that support parallel execution, like Gradle or Bazel

With these advanced build optimization techniques in your arsenal, you're ready to take your build process to the next level. But what about testing? Let's find out how you can make your testing process as efficient as possible.

In this article, you'll learn more about automation in DevOps and how to get started: How to get started with DevOps automation.

III. test optimization techniques: streamline your tests for a bulletproof pipeline

A. Test prioritization: every test run counts

Do you run your entire test suite every time, even if only a small part of the code base has changed? It's time to prioritize your tests and focus on what matters most to ensure the highest level of quality without wasting time and resources.

Techniques for intelligent prioritization of tests

  • Risk-based prioritization: Identify critical functionalities and prioritize tests accordingly
  • Time-based prioritization: schedule time for testing and run the most important tests first

Test prioritization tools: your guide to efficient testing

  • TestImpactAnalysis: A powerful tool that analyzes code changes and executes only the affected tests
  • Codecov: A test coverage analysis tool that identifies important tests for changed code

B. Test Automation: Accelerate Your Tests and Increase Confidence

Are you still testing your software manually? Automated testing is the key to faster test execution, fewer human errors, and more confidence in your pipeline.

The advantages of automated tests

  • Faster test execution
  • Consistent and repeatable results
  • Increased test coverage

Test Automation Frameworks: Your Path to Automated Excellence

  • Github Puppeteer: A popular choice for testing web applications
  • JUnit: The standard framework for Java applications
  • Pytest: A versatile and powerful framework for Python applications

C. Shift-Left Testing: Detect Bugs Early, Save Time and Effort

Why wait until the end of your pipeline to discover problems? Shift-Left Testing integrates testing earlier in the development process. So you can catch bugs earlier and save valuable time and resources.

The advantages of shift-left tests

  • Faster feedback loop for developers
  • Less time required for troubleshooting and error correction
  • Improved overall quality of the software

Implementing shift-left testing in your pipeline

  • Close cooperation between development and QA teams
  • Integrate automated testing into your CI process
  • Use static code analysis and linting tools

With these test optimization techniques, you'll ensure the quality of your software while maximizing efficiency. But what about deployment? Let's take a look at the latest strategies that will revolutionize your deployment process.

IV. Deployment Optimization Techniques: Seamless and Reliable Software Deployment

A. Continuous Deployment (CD): From code to production in the blink of an eye

Want to deliver features and bug fixes to your users faster than ever before? Continuous Deployment (CD) is the answer. By automating the deployment process, you can release new versions of your software as soon as they pass all tests, ensuring rapid deployment without sacrificing quality.

The advantages of Continuous Deployment

  • Shorter time to market
  • Faster feedback from users
  • Greater adaptability and responsiveness to market requirements

CD implementation tools: your gateway to fast releases

  • Spinnaker: A powerful multi-cloud CD platform
  • Harness: A modern, intelligent CD solution
  • GitHub Actions: A Versatile, Integrated CI/CD Tool for GitHub Users

B. Canary Releases: Protect your users with incremental rollouts

Worried about the impact of new releases on your users? With Canary Releases, you can deploy new versions of your software to a small percentage of users. This allows you to monitor performance and identify issues before rolling them out to all users.

The advantages of Canary Releases

  • Reduced risk of widespread problems
  • Faster identification and resolution of problems
  • Higher user satisfaction and greater trust

Implementing Canary Releases: The Art of Controlled Deployment

  • Use feature flags to manage incremental rollouts
  • Use of traffic control tools such as Istio or AWS App Mesh.
  • Monitor user feedback and application performance metrics

C. Blue/Green deployments: Minimizing Downtime and Maximizing Trust

Looking for a way to deploy new software releases with minimal impact to your users? At Blue/Green Deployments run two identical production environments that you can easily switch between and that don't cause downtime.

The advantages of Blue/Green Deployments

  • No downtime during releases
  • Simplified rollback in case of problems
  • Increased confidence in your deployment process

Blue/Green Deployment Tools: The key to smooth transitions

  • Kubernetes: Leverage powerful features like rolling updates and deployment strategies
  • AWS: Use services such as Elastic Beanstalk, ECS or EKS for seamless Blue/Green deployments.
  • Azure: Implement Blue/Green deployments with Azure App Service or AKS

When you use these advanced deployment methods, you ensure a smooth, reliable software delivery process that delights your users. But the optimization doesn't stop there. Let's explore the critical role of monitoring and feedback in your pipeline.

V. Monitoring and feedback: keep your finger on the pulse of your pipeline

A. The critical role of monitoring and feedback in optimization

How do you know if your pipeline is operating at maximum efficiency? Monitoring and feedback are key to continuous improvement. They allow you to measure performance, identify bottlenecks, and tune your pipeline for maximum impact.

B. Key Performance Indicators (KPIs): Key metrics

What should you measure to assess the health of your pipeline? By focusing on the right KPIs, you can gain valuable insights and identify areas for improvement.

Build-related KPIs

  • Build time
  • Build success rate
  • Length of the build queue

Test-related KPIs

  • Test execution time
  • Test coverage
  • Test error rate

Deployment-related KPIs

  • Frequency of use
  • Success rate of the operation
  • Mean time to recovery (MTTR)

C. Monitoring and feedback tools: Optimize with confidence

Now that you know what to measure, what tools can help you monitor your pipeline and gather valuable feedback?

Application Performance Monitoring (APM) Tools

  • Datadog: A comprehensive, all-in-one monitoring platform
  • New Relic: A powerful APM tool with a focus on observability and log and metrics management
  • AppDynamics: A business-oriented APM solution

Log and metrics management tools

  • Elastic Stack: A Versatile Suite for Log Analytics and Metrics Management
  • Grafana: A popular open source metrics visualization dashboard
  • Splunk: A robust platform for log analysis and operational intelligence

When you build monitoring and feedback into your pipeline, you gain valuable insights and can continuously optimize it. With these strategies, you're well on your way to building a truly efficient and effective DevOps pipeline.

VI. Conclusion: Embark on the journey to an optimized DevOps pipeline

Congratulations! You've now learned the advanced techniques you can use to optimize your build-test-deploy pipeline and realize the full potential of DevOps. From accelerating your build process to streamlining your testing and deployment, these strategies will pave the way for faster and more reliable software delivery.

Remember that the true spirit of DevOps is continuous improvement. When you apply these advanced techniques, you should constantly monitor, learn, and improve your pipeline. With this commitment, you'll stay ahead of the competition, delight your users, and drive your business to success.

Continuing Education: Your path to DevOps mastery

Want to dive deeper into these techniques and tools? Here are some resources to help you on your way:

Books and guides

  • "The DevOps Handbook" by Gene Kim, Jez Humble, Patrick Debois and John Willis.
  • "Continuous Delivery" by Jez Humble and David Farley.
  • "Accelerate" by Nicole Forsgren, Jez Humble and Gene Kim.

Online courses and tutorials

  • Coursera: "DevOps Culture and Mindset" and "Principles of DevOps".
  • Pluralsight: "Continuous Integration and Continuous Deployment" and "Mastering Jenkins".
  • Udemy: "Mastering DevOps with Docker, Kubernetes and Azure DevOps".

Get on the path to an optimized DevOps pipeline and remember that the road to mastery is paved with constant learning and improvement.

DevSecOps in the manufacturing industry

DevSecOps in the manufacturing industry

Industrial companies today are under increasing pressure to deliver high-quality products faster and more efficiently while ensuring operational security. DevSecOps, a set of principles and practices that bring together development, security and operations teams, can help companies meet these challenges and remain competitive in today's marketplace.

The manufacturing industry is changing - fast

Companies in the manufacturing industry are increasingly transforming themselves from traditional manufacturers to software companies and are experiencing a change in their processes and procedures. This change is driven by the growing importance of software in the manufacturing industry, because modern production processes cannot be changed without a change in IT.

This change brings with it challenges for manufacturing companies

One of the biggest challenges is changing culture and mindset. For example, traditional manufacturing companies work in isolated departments and follow established processes and practices, while software development requires a collaborative and agile approach. Another challenge is investing in new tools and technologies to support the software development process. This can involve significant upfront costs and force companies to change their business models.

According to a recent survey, 90 % of manufacturing companies are investing in software development as part of their digital transformation. In addition, 71% of manufacturing companies say that software is critical to their products and services. This trend is expected to continue and software spending in the manufacturing industry is expected to increase to $13.5 billion by the end of 2023.

The job market is changing

The shift to software-driven manufacturing is also changing the job market. Demand for software developers in the manufacturing sector increased by 20 % last year, and we expect it to continue to rise in the coming years. This reflects the shift toward software development as a core competency for manufacturing companies.

What is DevSecOps?

DevSecOps is a way of working that promotes collaboration and integration between different departments and task areas. It aims to optimize the software development process by continuously integrating and deploying small increments of code that are progressively tested and secured can be. This enables faster deployment of software and more regular updates, which increases the efficiency and agility of the business.

DevSecOps in the manufacturing industry

The manufacturing industry can apply DevSecOps to various areas of the business, such as supply chain management, quality control, and customer service. By automating manual and error-prone processes, manufacturers can reduce the risk of defects and improve the overall quality of their products. DevSecOps also helps companies ensure the security of their operations by integrating security practices into the development process.

There is hope

Despite these challenges, transforming into a software-enabled enterprise can bring significant benefits to the manufacturing industry. Using software development and DevSecOps principles, manufacturing companies can improve efficiency, safety and flexibility to remain competitive in today's marketplace. In addition, by addressing this transformation and the challenges it presents, these companies can position themselves for long-term success in the digital age.

Why is DevSecOps important in the manufacturing industry?

Traditional organizations have tended to operate in isolated departments where development, security and operations teams were separate and often isolated from each other. This can lead to slow and inefficient processes and an increased risk of errors and security vulnerabilities. DevSecOps helps break down these silos and promote collaboration and integration between departments.

By continuously integrating and deploying small sections of code, manufacturers can reduce the risk of defects and improve the overall quality of their products. DevSecOps also helps them stay competitive by enabling them to respond quickly to changing market conditions and customer needs. By deploying software faster and updating more frequently, companies can improve their responsiveness and agility.

How to implement DevSecOps in the manufacturing industry

Implementing DevSecOps in manufacturing organizations requires a culture shift and a change in mindset. Teams must collaborate, integrate their processes, and embrace automation and continuous improvement. Here are some steps to implementing DevSecOps in the manufacturing industry:

Start with a small, cross-functional team: 

Start with a small team representing different departments and functions, including development, security, and operations. This team can serve as a pilot group to test and refine the DevSecOps process.

Automate as much as possible: 

Automation can help reduce errors and increase efficiency. Consider automating manual and repetitive tasks such as testing and deploying software to free up time and resources for value-added activities.

Promote a culture of continuous improvement: 

Encourage teams to look for ways to continuously improve their processes and practices. Do this through regular retrospectives and incorporating feedback from different departments.

Invest in tools and technologies that support DevSecOps: 

There are many tools and technologies that can help manufacturers implement DevSecOps, such as version control systems, continuous integration and deployment platforms, and security testing tools. Investing in the right tools can help streamline the development process and improve security.

Train and educate teams on DevSecOps principles and practices: 

It is important that all team members know and understand the DevSecOps process. Training and education can help teams adopt and apply DevSecOps principles and practices.

Are there proven DevSecOps strategies for manufacturing companies?

Implementing DevSecOps in a manufacturing company requires a combination of the right strategies and tools to ensure success. Some proven strategies for manufacturing companies are:

Start small and expand step by step

Starting with a small, cross-functional team and testing the process before rolling it out is a more effective way to roll out DevSecOps than doing it across the enterprise at the same time. This approach allows organizations to test and refine their approach before rolling it out across the enterprise, reducing the risk of failure.

For example, a global automotive supplier began its DevSecOps journey with a small team focused on automating manual and repetitive tasks. The result of this pilot was a 20 % reduction in errors and a 25 % increase in efficiency.

Automate manual and repetitive tasks

Automation can significantly increase efficiency by reducing the need for manual, error-prone tasks. In addition, by automating processes such as testing and deployment, manufacturers can free up time and resources for more value-added activities.

A leading medical device manufacturer implemented automated test and delivery processes as part of its DevSecOps strategy. This resulted in a 50 % reduction in test time and a 30 % improvement in delivery speed.

Here you will learnhow companies manage to regularly release code (e.g. software updates and improvements) into the production environment.

Promoting a culture of continuous improvement

The key to successful DevSecOps implementation is to encourage teams to continuously improve their processes and practices. This can be achieved through regular retrospectives and the inclusion of feedback from different departments.

By holding weekly retrospectives and regularly soliciting feedback from its teams, an industrial equipment manufacturer fostered a culture of continuous improvement that led to a 15 % reduction in errors...

Investing in the right tools and technologies

There are many tools and technologies that can help manufacturers implement DevSecOps, such as version control systems, continuous integration and deployment platforms, and security testing tools. Investing in the right tools can help streamline the development process and improve security.

Common tools and technologies

Common tools used in DevSecOps include configuration management tools like Ansible and Chef, containerization tools like Docker and Kubernetes, Continuous Integration and Delivery (CI/CD) tools such as Jenkins and Travis CI, infrastructure-as-code tools such as Terraform, security testing tools such as OWASP ZAP and Burp Suite, and logging and monitoring tools such as Splunk and ELK Stack. These tools help companies automate tasks, streamline processes, and integrate security into the software development lifecycle.


DevSecOps can help manufacturing companies improve efficiency, security, and agility in today's fast-paced and competitive marketplace. Manufacturers can streamline their development process by breaking down silos, fostering cross-departmental collaboration and continuously delivering high-quality products. Implementing DevSecOps requires a culture shift, adoption of automation and continuous improvement practices, and investment in tools and technologies that support these principles. As manufacturing companies continue to adopt DevSecOps, we can expect to see more efficient and secure operations in the manufacturing industry.

Are you new to DevSecOps? Read our DevSecOps glossary to learn about the most important terms and technologies. Click here to go to the Glossary.

Glossary for DevSecOps

Glossary for DevSecOps

In recent years, DevSecOps has emerged as an important approach to software development that focuses on security throughout the software development lifecycle. DevSecOps combines development, security and operations into a unified and collaborative approach that helps teams develop secure software faster and more efficiently. As with many other areas, DevSecOps has its own terminology and set of acronyms that can be difficult for newcomers to navigate. In this article, we provide a comprehensive glossary of DevSecOps terms and definitions to help developers, security professionals and operations teams understand and communicate effectively in this rapidly evolving field.

Find out what DevSecOps is all about and why this approach is being used more and more here: The essential role of security in DevOps

About the glossary

In this glossary, we've compiled a list of common terms and concepts used in the context of DevSecOps, including Agile, continuous integration, continuous delivery, DevOps, security, vulnerabilities, penetration testing, and more. Understanding these terms is essential for anyone working in DevSecOps or wanting to learn more about this important area of software development and security.


A set of principles and practices for software development that emphasize flexibility, adaptability, and continuous improvement. Agile practices are often used in DevSecOps to enable rapid deployment of software updates and facilitate collaboration between development and security teams.

API Security:

The practice of securing application programming interfaces (APIs). APIs enable different systems and applications to communicate with each other. API security is a major DevSecOps concern because APIs often expose sensitive data and functionality to external systems. If APIs are not adequately secured, sensitive data can leak out. APIs can be secured using OAuth tokens and TLS encryption, for example.


A defect or error in a system or application that results in unexpected or undesirable behavior. Bugs can range from small problems that do not significantly affect the functionality of a system to large security holes that can be exploited by attackers. DevSecOps fixes security vulnerabilities (critical issues) before new features are finalized.


A network of servers, storage space and other resources is made available over the Internet so that users can access and use them on demand. Clouds can be public, meaning they are operated by a third-party provider and are accessible to a range of potential customers, or private, meaning they are operated by a company and are accessible only to that company.

Cloud Security:

The practice of securing systems, applications, and data in cloud computing environments. Cloud security is central to DevSecOps and involves the use of tools and practices such as encryption, access control, and network segmentation to secure cloud environments.

Code review:

A process in which one or more team members review code changes before they are incorporated into the main branch. Code reviews, regression testing, and test coverage help ensure that code changes are of high quality, meet coding standards, and are readable.


Compliance with regulatory standards and policies related to security, privacy, and other areas. In DevSecOps, regulatory compliance is often a key concern, and practices and tools are put in place to ensure that systems and applications comply with the appropriate regulations.

Configuration Management:

Manage, organize, and control system, application, and infrastructure configuration. Configuration management is commonly used in DevSecOps to ensure that systems are configured consistently and deployed in a repeatable and reliable manner. This also relates to Infrastructure as Code and the use of tools such as Terraform, Ansible, Puppet and Chef

Container security:

The practice of securing containerized applications and environments. Container security is a major DevSecOps concern because containers are commonly used in modern software development and delivery pipelines to deploy and distribute applications via containers.

Continuous Delivery (CD):

A software development practice in which code changes are automatically created, tested, and committed to production. (To ensure the integrity of engineers, CD changes are usually implemented in development and test systems, but changes in production may need to be approved manually).

CD differs from CI in that code changes must be ready for deployment at any time, whereas CI may require additional testing and validation before deployment.

Continuous Delivery also means that the software is always up to date and packaged ready to go into production.

Continuous Deployment:

A software development practice in which code changes are automatically created, tested, and deployed to production (or to a development system first) without manual intervention. Continuous deployment requires that code changes be thoroughly tested and validated before deployment to ensure that they do not introduce new bugs or vulnerabilities. Depending on the application, compliance regulations may also impact CD.

Continuous Integration (CI):

Continuous Integration (CI) is a software development practice in which code changes are often integrated into a common repository and the integrated code is automatically built and tested. The main goal of CI is to detect and fix integration problems early in the development process to reduce the risk of bugs and other problems in the final product.

With CI, the software packaging pipeline is run every time a code change is made, as you mentioned. This means that every change a developer makes to the code base is automatically built, tested, and packaged into a deployable artifact. The result is immediate feedback on whether the changes have caused any problems, and if so, what they are.

Continuous Monitoring:

The practice of continuously monitoring systems and applications for signs of security breaches, vulnerabilities, or other problems. Continuous monitoring helps organizations identify and respond to security threats and vulnerabilities in real time. It is an important component of DevSecOps.


A set of practices and tools aimed at improving collaboration between development and operations teams and accelerating the delivery of software updates. DevOps relies on automation and the use of tools such as Continuous Integration and Delivery to improve the speed and reliability of software updates.


A set of practices and tools aimed at integrating security practices into the software development and deployment process. It emphasizes collaboration between development, security, and operations teams. DevSecOps aims to build security into the software development lifecycle, rather than treating it as an afterthought.

Dynamic Analysis:

A type of software testing in which code is executed to identify bugs, vulnerabilities, and other issues. Dynamic analysis is commonly used in DevSecOps to verify the behavior of code in real-world scenarios and to identify issues that may not be detected during static analysis. In general, dynamic analysis analyzes and examines running applications. It allows you to review your applications and assess the risks or security vulnerabilities of third-party applications.

Incident Response:

Incident response is a process by which organizations identify, contain, and respond to security incidents or other unexpected events that could disrupt business operations. It is a coordinated effort among various teams and stakeholders. The purpose is to quickly identify, assess and mitigate the impact of an incident.

One of the most important tasks in incident response is to fix application failures, which can be caused by a variety of factors. For example, network problems, software errors, or security breaches. When an application fails, the emergency response team must act quickly to restore normal system operation. And thus prevent data loss or other negative impacts.


The hardware, software, and other resources that support the operation of a system or application. Infrastructure includes servers, storage, network devices, other hardware, and the software and tools used to manage and maintain these resources.

Infrastructure as Code (IaC):

A practice where the infrastructure configuration exists as code and is managed and versioned using the same tools and processes as the application code. With IaC, infrastructure can be more easily automated, tested and integrated into the software development and deployment process. In addition, the configuration can persist as code, which saves a lot of manual work and is an important part of automation in Ops.

Penetrations tests:

A type of security test in which an attacker simulates a real attack on a system or application to identify vulnerabilities and assess the security posture of the system. Penetration testing is often used in DevSecOps to identify and fix vulnerabilities before they can be exploited by real attackers.

Security as Code:

Security as Code is an approach to software development that integrates security practices into the software development lifecycle. It aims to make security a seamless and automated part of the development process. By embedding security checks and controls into the code itself, Security as Code aims to reduce the risk of security vulnerabilities and make it easier to maintain a secure infrastructure over time.

Unlike Infrastructure as Code (IaC), which focuses primarily on automating the creation and configuration of infrastructure resources, Security as Code goes beyond infrastructure automation and integrates security controls and policies into the code being developed.

Safety Automation:

The use of tools and processes to automate security tasks, such as vulnerability scanning, incident response, and compliance reporting. Security automation is an important part of DevSecOps and helps organizations improve the efficiency and effectiveness of their security practices.

Safety tests:

Testing systems and applications for vulnerabilities, weaknesses, and other security issues. Security testing is an important part of DevSecOps and can include penetration testing, vulnerability scanning, and code reviews.

Information Security:

The practice of protecting systems, networks, and data from unauthorized access, use, disclosure, disruption, modification, or destruction. Under DevSecOps, security practices are integrated into the software development and deployment process to ensure that software updates are secure and do not introduce new vulnerabilities. In addition, information security is about isolating and securing the runtime environment of live applications.

Software delivery process:

The process of developing, testing, and deploying software updates. The software delivery process typically involves a series of steps, including requirements gathering, design, coding, testing, and deployment, and may involve collaboration between development, testing, and operations teams. The software delivery process aims to deliver high-quality software updates in a timely and efficient manner.

Static Analysis:

A type of software testing that analyzes code without executing it to identify bugs, vulnerabilities, and other problems. Static analysis is commonly used in DevSecOps to detect and fix problems early in the software development process.

Test-Driven Development (TDD):

A software development practice in which tests are written for a portion of the code before the code itself is written. TDD helps ensure that the code is developed in a testable manner and meets the requirements defined by the tests.

Threat Modeling:

Identify, analyze, and prioritize potential security threats to a system or application. Threat modeling is widely used in DevSecOps to help organizations identify and address potential vulnerabilities before they can be exploited by attackers.

Vulnerability Scanning:

The practice of identifying vulnerabilities in systems and applications by scanning them for known vulnerabilities. Vulnerability scanning is often used in DevSecOps to help organizations identify and prioritize vulnerabilities that need to be fixed.


A vulnerability or gap in a system or application that could be exploited by an attacker to gain unauthorized access, disrupt service, or steal or manipulate data. As part of DevSecOps, vulnerabilities are identified and remediated as part of the software development and deployment process to prevent them from being exploited.

This glossary for DevSecOps gives you a first overview of the different terms and definitions for daily use. We are continuously expanding the glossary with additional and new terms.

Error analysis and troubleshooting of problems in the production environment of distributed systems

Guide: Troubleshooting and fixing production issues in distributed systems

Distributed systems are the backbone of many modern software applications and platforms. They enable service scalability and availability by distributing workloads across multiple machines and geographic locations. However, as with any complex system, production problems can disrupt service and impact users.

As a DevOps or Infrastructure Engineer, it is important to have the skills and knowledge to troubleshoot and resolve common production issues in a distributed system. These problems range from simple configuration issues to complex system architecture failures.

Without the ability to troubleshoot and resolve these issues, businesses can suffer significant consequences such as lost revenue, damage to their reputation, and decreased customer satisfaction.

But let's look at how you can fix common production problems in a distributed system.

Post-mortem analysis: Identifying the cause of the problem

In modern (microservices) deployments, software teams typically fix two different things during a post-mortem.

  • Traces of the network flow within the (microservice) components with tools like Kiali, Jaeger, Istio
  • Infrastructure components such as runtime, artifacts and more.

More importantly, modern software is developed to be self-healing. To achieve this, software teams ensure that the software is properly tested during the development phase, e.g. through unit tests and automated integration tests.

LetÔÇÖs dive into the process.

Information collection on the problem

The first step is to gather as much information as possible about the problem to determine the root cause of a production problem in a distributed system. This may involve analyzing logs, monitoring data and generated error messages. Log management and monitoring tools can help collect and organize this data so that it is easier to analyze.

To collect the information, you can use the following tools:

Identification of patterns and correlations

Once you have a clear understanding of the problem, the next step is to identify patterns and correlations that may point to the root cause of the problem. This may involve looking for trends or changes in the data that occurred around the time of the problem. Visualization tools and anomaly detection tools can also be helpful here, as they can help identify unusual patterns or deviations from normal behavior.

You can use the following tools to detect patterns and correlations:

  • Visualization tools: e.g. Kibana, Datadog
  • Anomaly detection tools: e.g. New Relic

Use of debugging tools and techniques

Once you understand the problem and the possible causes, it's time to start troubleshooting. This may involve using tools such as debuggers and profilers to understand what is happening at a deeper level within the system. It's also important to be systematic in your troubleshooting. Start with the most likely causes and work through the list until you find the root cause.

Essential tools for troubleshooting:

  • Debugger: e.g., GDB, LLDB
  • Profiler: e.g. perf, VTune

Set priorities and structure the troubleshooting process

One of the most important steps in troubleshooting and resolving production problems in distributed systems is prioritizing and organizing the process. This includes determining the impact of the problem on users and the system, and creating an action plan and timeline for resolution.

Determining the impact of the problem on the users and the system

To determine the impact of the problem, it is important to consider factors such as the number of users affected, the severity of the problem, and the potential consequences if the problem is not fixed. This information can be used to prioritize troubleshooting and problem resolution according to their importance.

Establish an action plan and a timetable for solving the problem

Once you have identified the impact of the problem, it is important to create an action plan and a timeline for resolution. This may require breaking down the troubleshooting into smaller, manageable tasks and setting a deadline for each task. It is also critical to include all necessary parties, such as developers and IT support, in the troubleshooting process and assign specific tasks to each team member.

By organizing and prioritizing the troubleshooting process, you can ensure that you resolve the issue promptly and efficiently, minimizing the impact on users and the system.

Fixing the problem

Temporary bug fixes to minimize impact on users

When a production problem occurs in a distributed system, it is important to minimize the impact on users. This may include temporary solutions, such as disabling certain features or redirecting traffic to another server until a permanent solution can be implemented.

It is important to carefully consider the possible consequences of a temporary solution and ensure that it does not cause further problems or complications. Also, try to inform users and affected parties about quick solutions so that they are aware of the situation and the potential impact.

Implementation of permanent solutions

Once the impact on users has been minimized through temporary solutions, the next step is to implement permanent solutions to address the root cause of the problem. This may involve changing the system architecture, updating software or hardware, or implementing new processes or procedures.

Any permanent solution must be carefully planned and tested to ensure that it is effective and practical. It may also be necessary to involve outside experts or vendors if the problem requires specialized knowledge or resources.

Testing and verification of the solution

Once a permanent solution has been implemented, it is important to thoroughly test and verify that the solution is effective and has no unintended consequences. For example, you can run stress tests, run simulations, or monitor the system to ensure that the problem does not reoccur.

Testing and verifying the problem resolution is an important step in troubleshooting, as it helps to fix the problem and ensure that the system is working correctly. In addition, it is important to document the testing and verification process for future reference.

Most importantly, modern software is developed to heal itself. To achieve this, software teams ensure that the software is properly tested during the development phase, e.g. through unit tests and automated integration tests.

These tests cover edge and corner cases.

The software project should include a QA team and have the following environments:

  • Development/Quality Assurance (dev/QA)
  • User acceptance testing (UAT, copy of prod)
  • Production environment

Once the code and tests are working in the development/quality assurance environment, it should be transferred to the UAT environment, which contains a copy of the production data (update process).

Good, structured code is essential in any software project. In this article you will find some tips: Read the article.

Review after an incident

Analyze the cause of the problem

After fixing a problem in a distributed system, it is essential to perform a follow-up investigation to determine the root cause and prevent similar problems from occurring.

This may include analyzing logs, monitoring data, and other relevant information to understand what caused the problem and how the root cause was resolved. It may also include gathering feedback from users and other stakeholders and performing root cause analysis such as the 5 Whys method.

The goal of the post-incident review is to identify any underlying issues or vulnerabilities in the system that may have contributed to the problem and take preventative measures to avoid similar situations in the future.

Implementing preventive measures to avoid similar problems in the future.

Once the cause of the problem has been identified, the next step is to take preventive measures to avoid similar situations. This may involve changing the system architecture, updating software or hardware, or introducing new processes or procedures.

It is important to carefully plan and test all preventive measures to ensure they are effective and do not have unintended consequences. It may also be necessary to involve outside experts or vendors if the problem requires specialized knowledge or resources.

Document the process for future reference

In addition to implementing preventive measures, it is important to document the entire troubleshooting process for future reference. This can help identify any patterns or common issues that occur in the system and can serve as a valuable resource for future troubleshooting efforts.

Documenting the process can also improve communication and collaboration between teams and serve as a learning opportunity for continuous improvement.


Troubleshooting and resolving production problems in distributed systems is critical to maintaining system functionality and reliability. This includes identifying the source of the fault, prioritizing and organizing the troubleshooting process, fixing the problem, and conducting a post-incident review to determine root causes and prevent similar problems.

Effective troubleshooting requires careful planning, attention to detail, and a proactive approach to continuous learning and improvement. By taking these steps, organizations can ensure that production issues are resolved promptly and efficiently, minimizing the impact on users and the system.

It is important to prioritize the resolution of production issues in distributed systems, as these issues can have significant consequences if left unaddressed. By taking a proactive approach to troubleshooting, organizations can maintain the reliability and functionality of their systems and provide a seamless experience for their users.