If cloud-native technologies and containers are on your radar, you’ve likely encountered Docker and Kubernetes and might be wondering how they relate to each other. Is it Kubernetes vs Docker or Kubernetes and Docker—or both?

What is the difference between Kubernetes and Docker?

Docker is a suite of software development tools for creating, sharing and running individual containers; Kubernetes is a system for operating containerized applications at scale.

Think of containers as standardized packaging for microservices with all the needed application code and dependencies inside. Creating these containers is the domain of Docker. A container can run anywhere, on a laptop, in the cloud, on local servers, and even on edge devices.

A modern application consists of many containers. Operating them in production is the job of Kubernetes. Since containers are easy to replicate, applications can auto-scale: expand or contract processing capacities to match user demands.

Docker and Kubernetes are mostly complementary technologies—Kubernetes and Docker. However, Docker also provides a system for operating containerized applications at scale, called Docker Swarm—Kubernetes vs Docker Swarm. Let’s unpack the ways Kubernetes and Docker complement each other and how they compete.

What is Docker?

Just as people use Xerox as shorthand for paper copies and say “Google” instead of internet search, Docker has become synonymous with containers. Docker is more than containers, though.

Docker is a suite of tools for developers to build, share, run and orchestrate containerized apps.

How does Docker work?

In Docker’s client-server architecture, the client talks to the daemon, which is responsible for building, running, and distributing Docker containers. While the Docker client and daemon can run on the same system, users can also connect a Docker client to a remote Docker daemon.

Developer tools for building container images

Docker Build creates a container image, the blueprint for a container, including everything needed to run an application – the application code, binaries, scripts, dependencies, configuration, environment variables, and so on. Docker Compose is a tool for defining and running multi-container applications. These tools integrate tightly with code repositories (such as GitHub) and continuous integration and continuous delivery (CI/CD) pipeline tools (such as Jenkins).

Sharing images

Docker Hub is a registry service provided by Docker for finding and sharing container images with your team or the public. Docker Hub is similar in functionality to GitHub.

Running containers

Docker Engine is a container runtime that runs in almost any environment: Mac and Windows PCs, Linux, and Windows servers, the cloud, and on edge devices. Docker Engine is built on top containerd, the leading open source container runtime, a project of the Cloud Native Computing Foundation (CNCF).

Built-in container orchestration

Docker Swarm manages a cluster of Docker Engines (typically on different nodes) called a swarm. Here the overlap with Kubernetes begins.

What is Kubernetes?

Kubernetes is an open source container orchestration platform for managing, automating, and scaling containerized applications. Kubernetes is the de facto standard for container orchestration because of its greater flexibility and capacity to scale, although Docker Swarm is also an orchestration tool.

How does Kubernetes work?

A Kubernetes cluster is made up of nodes that run on containerized applications. Every cluster has at least one worker node. The worker node hosts the pods, while the control plane manages the worker nodes and pods in the cluster.

What is Kubernetes used for?

Organizations use Kubernetes to automate the deployment and management of containerized applications. Rather than individually managing each container in a cluster, a DevOps team can instead tell Kubernetes how to allocate the necessary resources in advance.

Where Kubernetes and the Docker suite intersect is at container orchestration. So when people talk about Kubernetes vs. Docker, what they really mean is Kubernetes vs. Docker Swarm.

Benefits of using Kubernetes and Docker

When used together, Kubernetes and Docker containers can provide several benefits for organizations that want to deploy and manage containerized applications at scale.

Some of the key benefits of using include:

• Scalability: Kubernetes can scale containerized applications up or down as needed, ensuring they always have the resources they need to perform optimally. This is helpful for applications that experience a boost in traffic or demand.
• High availability: Kubernetes can ensure that containerized applications are highly available by automatically restarting containers that fail or are terminated. This can keep applications running smoothly and prevent downtime.
• Portability: Docker containers are portable, meaning they can be easily moved from one environment to another. This makes it easy to deploy containerized applications across different infrastructures, such as on-premises servers, public cloud providers, or hybrid environments.
• Security: Kubernetes can secure containerized applications by providing role-based access control, network isolation, and container image scanning. This can help to protect applications from unauthorized access, malicious attacks, and data breaches.
• Ease of use: Kubernetes can automate the deployment, scaling, and management of containerized applications. This can save organizations time and resources, and it can also help to reduce the risk of human error.
• Reduce costs: By automating the deployment and management of containerized applications, Kubernetes and Docker can help organizations reduce IT operations costs.
• Improve agility: Kubernetes and Docker can help organizations to be more agile by making it easier to deploy new features and updates to applications.
• Increase innovation: Kubernetes and Docker can help organizations to innovate more quickly by providing a platform that is easy to use and scalable.

Use cases for Kubernetes and Docker

When used in tandem, Kubernetes and Docker create a dynamic duo that unlocks a myriad of possibilities for seamless and scalable application deployment.

Here are a few use cases for using Kubernetes and Docker together:

• Deploying and managing microservices applications: Microservices applications are made up of small, independent components that can be easily scaled and deployed. Each microservice can be containerized using Docker, and Kubernetes can manage the deployment and scaling of these services independently. This allows for better maintainability, scalability, and fault isolation.
• Dynamic scaling: Together, Kubernetes and Docker enable dynamic scaling of applications. Kubernetes can automatically adjust the number of application instances based on demand. When traffic spikes, new containers can be spun up to handle the load, and when the load decreases, excess containers can be scaled down. This elasticity ensures efficient resource utilization and cost savings.
• Running containerized applications on edge devices: Kubernetes can be used to run containerized applications on edge devices, ensuring that they’re always available and up to date. Docker has redefined how applications are packaged and isolated. Docker eliminates the “it works on my machine” dilemma by encapsulating an application and its dependencies within a standardized container. This consistency ensures the application runs the same way across development, testing, and production environments.
• Continuous integration and continuous delivery (CI/CD): The combination of Docker and Kubernetes streamlines CI/CD pipelines. Docker images can be integrated into the CI/CD process, ensuring consistent testing and deployment. Kubernetes automates the deployment process, reducing manual intervention and accelerating the time to market for new features.
• Cloud-native applications: Docker and Kubernetes are cloud-agnostic, making it easier to deploy applications across different cloud providers or hybrid environments. This flexibility allows organizations to choose the most suitable infrastructure while avoiding vendor lock-in.

What are the challenges of container orchestration?

Although Docker Swarm and Kubernetes both approach container orchestration a little differently, they face the same challenges. A modern application can consist of dozens to hundreds of containerized microservices that need to work together smoothly. They run on multiple host machines, called nodes. Connected nodes are known as a cluster.
Hold this thought for a minute and visualize all these containers and nodes in your mind. It becomes immediately clear there must be a number of mechanisms in place to coordinate such a distributed system. These mechanisms are often compared to a conductor directing an orchestra to perform elaborate symphonies and juicy operas for our enjoyment. Trust me, orchestrating containers is more like herding cats than working with disciplined musicians (some claim it’s like herding Schrödinger’s cats). Here are some of the tasks orchestration platforms are challenged to perform.

Container deployment

In the simplest terms, this means to retrieve a container image from the repository and deploy it on a node. However, an orchestration platform does much more than this: it enables automatic re-creation of failed containers, rolling deployments to avoid downtime for the end-users, as well as managing the entire container lifecycle.

Scaling

This is one of the most important tasks an orchestration platform performs. The “scheduler” determines the placement of new containers so compute resources are used most efficiently. Containers can be replicated or deleted on the fly to meet varying end-user traffic.

Networking

The containerized services need to find and talk to each other in a secure manner, which isn’t a trivial task given the dynamic nature of containers. In addition, some services, like the front-end, need to be exposed to end-users, and a load balancer is required to distribute traffic across multiple nodes.

Observability

An orchestration platform needs to expose data about its internal states and activities in the form of logs, events, metrics, or transaction traces. This is essential for operators to understand the health and behavior of the container infrastructure as well as the applications running in it.

Security

Security is a growing area of concern for managing containers. An orchestration platform has various mechanisms built in to prevent vulnerabilities such as secure container deployment pipelines, encrypted network traffic, secret stores and more. However, these mechanisms alone are not sufficient, but require a comprehensive DevSecOps approach.

With these challenges in mind, let’s take a closer look at the differences between Kubernetes and Docker Swarm.

Kubernetes vs Docker Swarm

Both Docker Swarm and Kubernetes are production-grade container orchestration platforms, although they have different strengths.
Docker Swarm, also referred to as Docker in swarm mode, is the easiest orchestrator to deploy and manage. It can be a good choice for an organization just getting started with using containers in production. Swarm solidly covers 80% of all use cases with 20% of Kubernetes’ complexity.

A swarm is made up of one or more nodes, which are physical or virtual machines running in Docker Engine.

Swarm seamlessly integrates with the rest of the Docker tool suite, such as Docker Compose and Docker CLI, providing a familiar user experience with a flat learning curve. As you would expect from a Docker tool, Swarm runs anywhere Docker does and it’s considered secure by default and easier to troubleshoot than Kubernetes.

Kubernetes, or K8s for short, is the orchestration platform of choice for 88% of organizations. Initially developed by Google, it’s now available in many distributions and widely supported by all public cloud vendors. Amazon Elastic Kubernetes Service, Microsoft Azure Kubernetes Service, and Google Kubernetes Platform each offer their own managed Kubernetes service. Other popular distributions include Red Hat OpenShift, Rancher/SUSE, VMWare Tanzu, IBM Cloud Kubernetes Services. Such broad support avoids vendor lock-in and allows DevOps teams to focus on their own product rather than struggling with infrastructure idiosyncrasies.

The true power of Kubernetes comes with its almost limitless scalability, configurability, and rich technology ecosystem including many open-source frameworks for monitoring, management, and security.

Docker and Kubernetes: Better together

Simply put, the Docker suite and Kubernetes are technologies with different scopes. You can use Docker without Kubernetes and vice versa, however they work well together.

From the perspective of a software development cycle, Docker’s home turf is development. This includes configuring, building, and distributing containers using CI/CD pipelines and DockerHub as an image registry. On the other hand, Kubernetes shines in operations, allowing you to use your existing Docker containers while tackling the complexities of deployment, networking, scaling, and monitoring.

Although Docker Swarm is an alternative in this domain, Kubernetes is the best choice when it comes to orchestrating large distributed applications with hundreds of connected microservices including databases, secrets, and external dependencies.

How does advanced observability benefit Kubernetes and Docker Swarm?

Whether you’re using Kubernetes or Docker Swarm, or both, managing clusters at scale comes with unique challenges, particularly when it comes to observability. Application teams and Kubernetes/Swarm platform operators alike depend on detailed monitoring data. Here are some examples.

Kubernetes provides some very basic monitoring capabilities, like event logs and CPU loads for example. However, there’s a growing number of open-standard and open-source technologies available to augment Kubernetes’ built-in features. Some frequently used observability tools include: Promtail, Fluentbit and Fluentd for logs; Prometheus for metrics; and OpenTelemetry for traces, to name a few.

Dynatrace integrates with all these tools and more, and adds its own high-fidelity data to create a single real-time entity model. This unique capability enables Dynatrace to provide advanced analytics, AI-powered root-cause-analysis and intelligent automation, providing application teams and platform operators a unified view on the full technology stack.

The post Kubernetes vs Docker: What’s the difference? appeared first on Dynatrace news.

Dynatrace introduced the Dynatrace Operator, built on the open source project Operator Framework, in late 2018. The Dynatrace Operator enables the deployment, configuration, maintenance, and upgrade of Dynatrace full stack observability in Kubernetes and Red Hat OpenShift clusters.

We’re delighted to share that IBM and Dynatrace have joined forces to bring the Dynatrace Operator, along with the comprehensive capabilities of the Dynatrace platform, to Red Hat OpenShift on the IBM Power architecture (ppc64le). IBM Power servers enable customers to respond faster to business demands, protect data from core to cloud, and streamline insights and automation. By leveraging the Dynatrace Operator and Dynatrace capabilities on Red Hat OpenShift on IBM Power, customers can accelerate their modernization to hybrid cloud and increase operational efficiencies with greater visibility across the full stack from hardware through application processes.

Dynatrace full stack Red Hat OpenShift observability

Dynatrace unifies platform engineering and application teams on a single platform, enhancing software quality and operational efficiency to drive innovation. Red Hat OpenShift monitoring with Dynatrace stands out due to the following key aspects:

1. Infrastructure health monitoring and optimization: Assess the status of your infrastructure at a glance, understand resource consumption, optimize resource allocation for cost-efficiency, and track software versions running within the Kubernetes environment.
2. Full stack observability: Gain comprehensive observability across the entire stack, including Kubernetes clusters, applications, and underlying infrastructure. Captures metrics, traces, logs, and other telemetry data in context. Additionally, Dynatrace integrates seamlessly with cloud-native technologies and services, such as Istio and Prometheus, further enhancing its monitoring capabilities.
3. Smartscape topology mapping: Dynatrace uses its Smartscape technology to semantically map metrics, traces, logs, and real user data to specific Kubernetes objects, including containers, pods, nodes, and services. Having all data in context tremendously simplifies analytics and problem detection.
4. Intelligent root cause analysis: Use Davis® AI to automatically detect and analyze performance issues across the entire tech stack. Davis can identify problematic components or configurations and provide insights into the root cause of issues, enabling faster troubleshooting and resolution.
5. Scalability and cloud-native support: Dynatrace is designed to scale effortlessly in dynamic Kubernetes environments. Supporting GitOps practices, it can handle large-scale deployments with thousands of nodes and containers, making it suitable for enterprise-grade Red Hat OpenShift monitoring.

“Dynatrace offers a trusted and certified solution for monitoring applications running on Red Hat OpenShift using AI-powered root-cause analysis. Combining Dynatrace’s insights with Red Hat OpenShift, the industry’s leading hybrid cloud application platform powered by Kubernetes, allows organizations to more easily run decoupled services and applications, which can be a monitoring nightmare.”

Chris Morgan – Senior Director, Customer & Field Engagement, Red Hat Hybrid Platforms

Dynatrace Operator for OneAgent, API monitoring, routing, and more

The Dynatrace Operator leverages Kubernetes’ native capabilities to automate the deployment, configuration, and lifecycle management of Dynatrace components within Kubernetes environments. It simplifies the process of setting up and maintaining Dynatrace observability by encapsulating the necessary configuration and operational logic into a single entity.

The Operator acts as an intelligent controller that understands the desired state of the Dynatrace monitoring environment and takes actions to achieve the desired state. It automates tasks such as provisioning and scaling Dynatrace monitoring components, updating configurations, and ensuring the health and availability of the monitoring infrastructure.

The Dynatrace Operator deploys and auto-configures the following resources:

• OneAgent, deployed as a DaemonSet, collects host metrics from Kubernetes nodes. It also detects new containers and injects OneAgent code modules into application pods.
• Dynatrace ActiveGate is used for routing, as well as for monitoring Kubernetes objects by collecting data (metrics, events, status) from the Kubernetes API.
• Dynatrace webhook server validates Dynakube definitions for correctness.

With this approach:

• Red Hat OpenShift infrastructure (control plan and worker nodes) and workloads are instrumented automatically without any manual code change.
• The scope of the automatic workload instrumentation is user configurable, for example, by namespace.
• Lifecycle management of Dynatrace components (ActiveGate, OneAgent) is automatic and secure.

“With Dynatrace, customers can leverage comprehensive, full-stack observability as they modernize applications with Red Hat OpenShift on IBM Power. These features can enable customers to take full advantage of the excellent performance of containerized workloads on IBM Power, optimizing resource utilization for maximum efficiency.”

Gina King – Director ISV Ecosystem and Alliances, IBM

Get Started with monitoring Red Hat OpenShift on IBM Power

• If you’re already a Dynatrace customer, refer to our updated Kubernetes deployment instructions.
• New to Dynatrace? Sign up for a fully-functional Dynatrace free trial.

Please reach out to the following contacts for additional insights and information.

Dynatrace
Tim Stensby, Senior Manager, Strategic Alliances
tim.stensby@dynatrace.com

Red Hat
Mark Vincent
mvincent@redhat.com

IBM
Sara Cohen
cohends@us.ibm.com

The post Dynatrace observability is now available for Red Hat OpenShift on the IBM® Power® architecture appeared first on Dynatrace news.

If you’re evaluating container orchestration software to manage containerized applications at scale, you may be wondering about the differences between OpenShift and Kubernetes. But as you contemplate OpenShift vs. Kubernetes, it’s important to understand what these container orchestration solutions are, how they relate, and their benefits and drawbacks.

A guide to container orchestration software

Container orchestration software automates the administration of containerized workloads and services, greatly reducing the time IT staff spend keeping an application environment running smoothly. Container orchestration allows an organization to digitally transform at a rapid clip without getting bogged down by slow, siloed development, difficult scaling, and high costs associated with optimizing application infrastructure.

As with Kubernetes vs. Docker, OpenShift vs. Kubernetes is a common debate, as they are two of the most widely used container orchestration tools. Although they share many features in common, there are also critical differences between OpenShift and Kubernetes. To put those differences into perspective, let’s look at what Kubernetes and OpenShift are and how they work.

What is Kubernetes?

Kubernetes is an open source container orchestration platform that enables organizations to automatically scale, manage, and deploy containerized applications in distributed environments. According to the Kubernetes in the Wild 2023 report, “Kubernetes is emerging as the operating system of the cloud.” In recent years, cloud service providers such as Amazon Web Services, Microsoft Azure, IBM, and Google began offering Kubernetes as part of their managed services. As a result of these services, organizations can further streamline the administrative overhead associated with application development.

Kubernetes containers are portable across environments, which enables developers to run them on nearly any type of infrastructure, whether in the cloud or locally. This flexibility helps organizations avoid vendor lock-in. Kubernetes also gives developers freedom of choice when selecting operating systems, container runtimes, storage engines, and other key elements for their Kubernetes environments. They can integrate their own applications in the Kubernetes API or use Kubernetes’ own tooling to roll out new features. One major Kubernetes advantage is its self-healing, continually making repairs and addressing failures that affect applications’ integrity.

That said, Kubernetes has some drawbacks. Its inherent complexity makes observability difficult — especially when used across highly distributed systems. IT teams can’t see into the internal state of Kubernetes containers, so they often collect a wide variety of telemetry data — such as logs, metrics, and distributed traces — to compensate for this lack of visibility.

While these data sources are helpful, they often can’t help IT understand the relationships and context necessary to quickly identify the root causes of application performance issues. As a result, organizations can have trouble transforming at scale, improving critical service-level agreements, and optimizing the user experience.

What is OpenShift?

Like Kubernetes, OpenShift is an open source Kubernetes-based container platform. OpenShift is developed by Red Hat and can run in a variety of environments — both cloud and on premises. In fact, it is a frequent choice for running Kubernetes on premises.

Because it’s based on Kubernetes, OpenShift provides containerization and orchestration of containerized workloads. Like Kubernetes, it allocates resources efficiently and ensures high availability and fault tolerance.

But OpenShift builds from there to provide integrated development tools, CI/CD (continuous integration/continuous deployment) pipelines, and built-in support for popular programming languages, frameworks, and databases. These tools enable OpenShift to support the entire application lifecycle, from development to production, including scaling, rolling updates, and version control. Without having to worry about underlying infrastructure concerns, such as storage, security, and lifecycle management, developers can focus on writing code.

Likewise, Red Hat OpenShift helps organizations administer Kubernetes more efficiently. For example, OpenShift simplifies Kubernetes management tools, giving developers everything they need to manage Kubernetes nodes, as well as the underlying control plane.

In addition, OpenShift provides numerous cloud services and self-managed deployment models to suit various applications and architectures, including the following:

• OpenShift Container Platform (OCP). This self-managed offering can run on premises or in the cloud.
• OpenShift Dedicated (OSD). The managed service runs on public clouds such as Amazon Web Services and Google Cloud.
• Red Hat OpenShift Online (OSO). This fully managed service runs on Red Hat’s public cloud.

Despite its advantages, however, OpenShift also has its limitations. While Kubernetes supports all cloud and Linux distributions, making it widely accessible to organizations using various platforms, OpenShift supports only Red Hat distributions, such as Red Hat Enterprise Linux (RHEL), CentOS, and Fedora. As a commercial solution, OpenShift is also comparatively less flexible than open source Kubernetes, making it less customizable to an organization’s unique requirements.

OpenShift vs. Kubernetes: Weighing the key differences

While Kubernetes and OpenShift are both popular container orchestration platforms, they are used in slightly different ways and offer different features. Some of the key differences include the following:

• Origin. Originally created by Google, Kubernetes is an open source project managed by the Cloud Native Computing Foundation (CNCF). OpenShift, on the other hand, is an open source Red Hat offering that is built on top of Kubernetes primarily on RHEL operating systems.
• Ease of use. While Kubernetes offers increased flexibility and powerful features, it can be complex to set up and manage. In contrast, OpenShift provides a simplified, user-friendly interface, with built-in support for CI/CD pipelines.
• Security. OpenShift has several built-in security features, while Kubernetes relies on the underlying infrastructure and additional tools for security. Additionally, OpenShift runs containers as a non-root user by default and provides additional security policies out of the box.
• Networking. Kubernetes provides a basic networking model. However, it needs additional tools or plugins for more advanced networking features. OpenShift, on the other hand, includes a more advanced software-defined networking (SDN) solution, which supports network policies for finer control over container communication.
• Updates and support. Kubernetes has frequent updates, which can sometimes lead to issues such as breaking changes. Red Hat OpenShift offers long-term support versions and commercial support.
• Integration and extensions. Kubernetes is more of a bare-bones platform. Therefore, it relies on external tools and services for most integrations and extensions. Conversely, as a Red Hat offering, OpenShift provides built-in integration with other Red Hat products and offers a marketplace for third-party extensions.
• Pricing. Unlike Kubernetes, which is a completely free and open source service, OpenShift has a pricing model for its enterprise version that includes additional features, support, and services.

A lesson in terminology: Kubernetes namespace vs. OpenShift project

It’s OpenShift vs. Kubernetes when it comes to terminology, too. In addition to the aforementioned feature differences, Kubernetes and OpenShift use different terminology, which can be confusing for organizations and practitioners alike.

For example, a namespace in Kubernetes is typically referred to as a project in OpenShift. Despite their similarities, there are a few differences between namespaces and projects, including the following:

• Access control. In Kubernetes, users manage access control independently from namespaces. In contrast, OpenShift’s projects have a predefined set of permissions for project-level operations. This makes it easier for organizations to control who has access to what within a project.
• Isolation. While teams use both namespaces and projects to isolate resources within a cluster, OpenShift’s projects provide additional features. These include the ability to limit the amount of resources that all containers can consume within a project.
• User-friendly. Unlike Kubernetes namespaces, OpenShift projects are more user-friendly. When a user creates a project, for instance, they automatically become the project admin. With Kubernetes namespaces, this does not happen automatically.

Which container orchestration software is right for you?

If your organization needs a container orchestration solution with enterprise-level support and security, OpenShift is the clear choice. OpenShift offers a secure-by-default option to increase security, and its security policies are much stricter than Kubernetes. OpenShift is also a good choice if CI/CD is a priority for your organization.

Additionally, OpenShift is designed to meet the needs of industries with strong compliance and regulatory requirements, such as healthcare or finance. It addresses regulations such as the European Union’s General Data Protection Regulation and the U.S. Health Insurance Portability and Accountability Act.

On the other hand, Kubernetes is a strong option if you need more customization and flexibility, and you have in-house Kubernetes experts who can troubleshoot problems as they arise.

Kubernetes also works on the widest possible range of operating systems and platforms. If you’re a social media or gaming company that places an especially high priority on releasing updates at a rapid pace, Kubernetes may be a better fit.

Automatic and intelligent observability for OpenShift and Kubernetes

Whether you choose OpenShift vs. Kubernetes or vice versa, Dynatrace can make the most of your container orchestration solution. Dynatrace uses AIOps and cloud observability to combine metrics, logs, and traces with topology information, real user experience data, and meta information. With advanced observability of every Kubernetes cluster, pod, and node — and all connections and dependencies they touch — you can quickly pinpoint and solve performance problems as they arise.

Additionally, Dynatrace offers powerful monitoring capabilities for OpenShift, helping you manage costs, automate your operations, and release better software faster.

Whether using OpenShift or Kubernetes, the Dynatrace observability and security platform is the only Kubernetes monitoring system with continuous automation that identifies and prioritizes alerts from applications and infrastructure without changing code, container images, or deployments.

The post OpenShift vs. Kubernetes: Understanding the differences appeared first on Dynatrace news.

Update: The new Kubernetes in the Wild report 2025 is now available.

Kubernetes adoption survey executive summary

Modern cloud-native computing is impossible to separate from containers and Kubernetes adoption. Although Kubernetes is still a relatively young technology, a majority of global enterprises use it to run business-critical applications in production. Its rapid adoption is driven—and challenged by—an ever-growing ecosystem of Kubernetes technologies that add advanced platform features, such as security, microservices communication, observability, scaling, resource utilization, and so on.

The Kubernetes in the Wild survey reveals how organizations actually use Kubernetes in production. The study analyzes factual Kubernetes production data from thousands of organizations worldwide that are using the Dynatrace unified observability and security platform to keep their Kubernetes clusters secure, healthy, and high-performing.

Findings provide insights into Kubernetes practitioners’ infrastructure preferences and how they use advanced Kubernetes platform technologies. The report also reveals the leading programming languages practitioners use for application workloads. As Kubernetes adoption increases and it continues to advance technologically, Kubernetes has emerged as the “operating system” of the cloud.

1. Kubernetes moved to the cloud in 2022
2. Kubernetes infrastructure models differ between cloud and on-premises
3. Kubernetes is emerging as the “operating system” of the cloud
4. The strongest Kubernetes growth areas are security, databases, and CI/CD technologies
5. Open-source software drives a vibrant Kubernetes ecosystem
6. Java, Go, and Node.js are the top 3 programming languages for Kubernetes application workloads

Kubernetes moved to the cloud in 2022

In 2022, Kubernetes became the key platform for moving workloads to the public cloud. At an annual growth rate of +127 percent, the number of Kubernetes clusters hosted in the cloud grew about five times as fast as clusters hosted on-premises. Likewise, the share of cloud-hosted clusters increased from 31% in 2021 to 45% in 2022. Cloud-hosted Kubernetes clusters are on par to overtake on-premises deployments in 2023.

Most Kubernetes clusters in the cloud (73%) are built on top of managed distributions from the hyperscalers like AWS Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS), or Google Kubernetes Engine (GKE). Accordingly, the remaining 27% of clusters are self-managed by the customer on cloud virtual machines.

Kubernetes hosting decisions are guided by a set of parameters, including cost, ease of provisioning and scaling, data security, and regulatory compliance. As hyperscalers invest in all these areas and expand their presence into more geographic regions, they become more attractive to a broader set of organizations.

Kubernetes infrastructure models differ between cloud and on-premises

A typical cluster running in the public cloud consists of 5 relatively small nodes with just 16 to 32 GB of memory each. In comparison, on-premises clusters have more and larger nodes: on average, 9 nodes with 32 to 64 GB of memory.

The different infrastructure setup reflects economic and technical considerations. Hyperscalers offer a competitive price point for small to medium-sized hosts. Through effortless provisioning, a larger number of small hosts provide a cost-effective and scalable platform. On-premises data centers invest in higher capacity servers since they provide more flexibility in the long run, while the procurement price of hardware is only one of many cost factors.

Kubernetes is emerging as the “operating system” of the cloud

As the ideal orchestration platform for running cloud-native microservice applications, Kubernetes comes with the benefit of built-in deployment, scaling, and resiliency capabilities. In 2021, in a typical Kubernetes cluster, application workloads accounted for most of the pods (59%). By contrast, all non-application workloads, such as system and auxiliary workloads, played a relatively smaller part.

But in 2022, this picture reverses. As Kubernetes adoption has grown, auxiliary workloads now outnumber application workloads (63% vs. 37%). This switch reflects that organizations are implementing more advanced Kubernetes platform technologies such as security controls, service meshes, messaging systems, and observability tools. At the same time, organizations are using Kubernetes for a broader range of use cases, including build pipelines and scheduled utility workloads, among others. Kubernetes becomes the platform for running almost anything. As such, Kubernetes is emerging as the “operating system” of the cloud.

“At Dynatrace, we use Kubernetes for any new software project, from build pipelines to SaaS offerings. We also see the same trend with our customers. Kubernetes effectively has emerged as the operating system for the cloud.”

Anita Schreiner, Dynatrace VP Delivery

Strongest Kubernetes growth areas are security, databases, and CI/CD technologies

In 2022, organizations identified Kubernetes security as a top priority. Starting from a low baseline, the percentage of organizations using Kubernetes security tools increased from 22% in 2021 to 34% in 2022. This corresponds to an annual growth rate of +55%. That trend will likely continue as Kubernetes security awareness further rises and a new class of security solutions becomes available.

Of the organizations in the Kubernetes survey, 71% run databases and caches in Kubernetes, representing a +48% year-over-year increase. Together with messaging systems (+36% growth), organizations are increasingly using databases and caches to persist application workload states.

Continuous integration and delivery (CI/CD) technologies grew by +43% year-over-year. This trend shows that organizations are dedicating significantly more Kubernetes clusters to running software build, test, and deployment pipelines.

“The immense growth of Kubernetes presents new security challenges in runtime and increased complexity in hardening CI/CD pipelines in development. On the upside, new application security approaches address these challenges, reducing exposure to attacks and mitigating risks.”

Andreas Berger, Dynatrace Senior Principal Application Security

Open source software drives a vibrant Kubernetes ecosystem

Focusing on non-application workloads, organizations use an increasing variety of technologies. These results reflect the need to enhance Kubernetes with better observability, security, and service-to-service communications. Similarly, other technologies enable specific use cases like CI/CD tools or databases. Across all categories in the Kubernetes survey, open source projects rank among the most frequently used solutions.

• Open source observability: Prometheus is the clear leader in open source observability and is used by 65% of organizations. In general, metrics collectors and providers are most common, followed by log and tracing projects. Note: The survey excluded all commercial observability offerings, including Dynatrace.
• Databases: Among databases, Redis is the most used at 60%. Redis is an in-memory key-value store and cache that simplifies processing, storage, and interaction with data in Kubernetes environments. Accordingly, for classic database use cases, organizations use a variety of relational databases and document stores.
• Messaging: RabbitMQ and Kafka are the two main messaging and event streaming systems used. Specifically, they provide asynchronous communications within microservices architectures and high-throughput distributed systems.
• Continuous integration and delivery: ArgoCD, Flux, GitLab, and Jenkins are the most widely adopted CI/CD tools. Organizations increasingly use the flexibility and elasticity of Kubernetes to run CI and CD jobs as well as their control planes.
• Big data: To store, search, and analyze large datasets, 32% of organizations use Elasticsearch.
• Security: For security, organizations mostly use policy checkers and enforcers, such as Gatekeeper. The need for runtime security observability is growing to automate vulnerability impact analysis.
• Service meshes: Istio is the most used service mesh. Organizations are increasingly using service meshes in large Kubernetes clusters to automate secure service-to-service communication and expose telemetry data for better observability.

“Dynatrace believes in a strong open-source ecosystem and embraces the adoption of cloud-native technologies and practices. That’s why we actively contribute to and bootstrap projects and participate in the open source community in various roles. Dynatrace’s investment in open source technologies keeps growing.”

Alois Reitbauer, Dynatrace Chief Technology Strategist

Java, Go, and Node.js are the top Kubernetes programming languages

The Dynatrace OneAgent automatically detects the specific programming languages of every individual application workload running on Kubernetes. This provided unique insights into the Kubernetes programming languages organizations use.

Java Virtual Machine (JVM)-based languages are predominant. Accordingly, 65% of all application workloads run in a JVM, including related application servers like Tomcat or Spring. Most organizations, 72%, use Java to some degree.

Go ranks number 2 with a 58% adoption rate among organizations, with 14% of application workloads written in Go. Not counted are Kubernetes system workloads, sidecars, or any standard components of non-application workloads. In addition, Node.js ranks third in terms of workload count and organizational adoption.

“With Kubernetes, polyglot programming finally becomes a reality. As a result, Kubernetes empowers existing teams and makes onboarding of new ones easy, regardless of programming language and framework usage.”

Florian Ortner, Dynatrace Chief Product Officer

Kubernetes survey methodology

This report reflects Kubernetes adoption statistics based on the analysis of 4.1 billion Kubernetes pods from thousands of Dynatrace customers in all global regions. The data covers the period of January 2021 through September 2022. These customers are among the world’s largest 15,000 organizations from all major industries, including financial services, retail and e-commerce, technology, transportation, manufacturing, healthcare, and public-sector organizations.

The report only includes production data from Dynatrace customers and excludes all Kubernetes clusters Dynatrace uses internally or for hosting SaaS offerings.

The post Kubernetes in the Wild report 2023 appeared first on Dynatrace news.

What is Fluentd?

Fluentd is an open-source data collector that unifies log collection, processing, and consumption. It collects, processes, and outputs log files to and from a wide variety of technologies. The result is an easily configurable log pipeline that supports a continuously expanding set of plugins:

• Input plugins collect logs from pods, apps, DBs, load balancers, etc.
• Processing plugins parse (normalize), filter, enrich (tagging), format, and buffer log streams
• Output plugins deliver logs to storage solutions, analytics tools, and observability platforms like Dynatrace

Fluentd can run as a DaemonSet in a Kubernetes cluster. Its behavior can be controlled via a fluentd.conf file. Built-in resiliency ensures data completeness and consistency even if Fluentd or an endpoint service goes down temporarily.

While Fluentd solves the challenges of collecting and normalizing Kubernetes events and logs, Kubernetes performance and availability problems can rarely be solved by investigating logs in isolation. Moreover, domain knowledge is required to sift through the vast amount of disconnected log files and find the proverbial needle in the haystack.

Bring the full value of Dynatrace to Fluentd

By treating Fluentd as a first-class citizen, Dynatrace brings its full platform power to bear: best-in-class end-to-end observability, automated anomaly detection, and AI-powered root-cause determination.

The Dynatrace platform is substantially different because it automatically captures the topology and dependencies of an environment. By linking all observability data—including Fluentd logs—into meaningful context with each monitored entity, the causal relationship between observability data can be modeled and understood, which enables:

• Significantly faster ad-hoc problem debugging by analyzing Fluentd logs in the full context of topology, metrics, traces, user sessions, and more.
• Precise, AI-powered anomaly root-cause determination based on automatic log analysis and custom user-defined events.
• Detailed performance analysis for better software architecture and resource allocation

For example, say you find multiple error events in different log files. Time-based correlations can only tell you that there is some concurrency. But given the hundreds of pods and processes in modern systems, these errors might not be logically connected. Dynatrace eliminates this ambiguity by indicating the error events that are truly related. All metrics, traces, and real user data are also surfaced in the context of specific events.

With Dynatrace, you can create custom metrics based on user-defined log events. For example, you can count specific error or alert events and chart them, create Service Level Indicators for SLO tracking, or feed the events back to the Dynatrace Davis AI-engine for refined root-cause analysis.

Should I use Fluentd or OneAgent for Kubernetes?

To be clear, you don’t need Fluentd to capture Kubernetes logs. The Dynatrace Operator for Kubernetes includes a native OneAgent DaemonSet that serves as a fantastic log agent. So, let’s compare the two approaches for ingesting logs.

First and foremost, both approaches equally enrich Kubernetes logs with metadata for enhanced analytics. Both approaches automatically add pod name, namespace, container name, tags, labels, and more to each log file. In addition, logs are enriched with associated transaction traces. This enables all the advanced use cases described above.

The main advantage of using the Dynatrace OneAgent approach is its ease of deployment. Log monitoring works out-of-the-box with no further configuration needed. The Dynatrace Operator automates the roll-out of all Dynatrace components, including the OneAgent DaemonSet, across clusters and manages all aspects of observability.

Fluentd, on the other hand, offers a large ecosystem of plugins and the flexibility of log processing pipelines. If you’re already using Fluentd and are happy with it, the Fluentd approach might make the most sense for you. Adding the Dynatrace plug-in is easy. Fluentd also enables two advanced use cases:

• You can stream logs to different Dynatrace environments from the same Kubernetes cluster. For example, you can send application pod logs to a different environment than the Kubernetes node logs.
• Support for streaming logs in application-only integrations.

Fluentd logs in context: Example use cases

Workload performance and resource utilization

The following Kubernetes workload overview page is a powerful example of the kind of insights you get with all relevant data in one place: workload properties, pod and container metrics (for example, CPU and memory utilization), services metrics (for example, response time and failure rate), and Kubernetes events and logs. The logs are expandable and show the payload, all metadata, attributes, and related traces. With all this data in one place, it’s easy to optimize resource limits or understand the service performance under varying load conditions.

Transaction breakdowns

With multiple microservices involved in a single user transaction, the true root cause of a breakdown can be hard to find. In such cases, you can select the logs of one workload, find the entire transaction, and display all logs related to that transaction. This way, it’s easy to spot and understand the source of problems.

To get started, expand any log entry in the Log Viewer and select Show related trace to navigate to the related trace.

Then, on the corresponding distributed trace overview page, select View logs for this source to display all logs related to the distributed transaction.

Set up alerting for recurring log patterns

Dynatrace has long provided automatic baselining, alerting, and root-cause analysis for a wide range of error conditions. Now you can use logs to create custom, application-specific alerts. In the example below, a user configured Davis to alert on problems with DNS queries in a Kubernetes cluster. To accomplish this, the user recoded DNS query responses in a dedicated log file and created a custom metric for the number of search lines with an alerting threshold of 2. With this simple configuration, Davis automatically creates a problem ticket whenever the defined anomaly occurs. Take a look at this blog post to learn more about how to transform log data into actionable metrics and have Davis AI do the work for you.

In summary, these examples show that:

• Logs, whether ingested through Fluentd or OneAgent, significantly improve the observability of complex Kubernetes environments.
• Dynatrace automatically enriches logs with metadata and analyzes them in context with corresponding metrics and traces.
• Context and topology mapping are essential for both automatic and ad hoc root-cause analysis.
• Logs can be used to enhance Davis AI analysis with application-specific problem patterns.

Get started today

Deploying the Dynatrace Fluentd output plugin is easy. You’ll find detailed instructions on both the Dynatrace Hub and GitHub.

There is also an excellent video tutorial available on the Is It Observable YouTube channel.

The post Connect Fluentd logs with Dynatrace traces, metrics, and topology data to enhance Kubernetes observability appeared first on Dynatrace news.

Infrastructure monitoring is the process of collecting critical data about your IT environment, including information about availability, performance, and resource efficiency. The goal? Reducing downtime, improving user experience, speed, reliability, and flexibility, and ensuring IT investments are delivering on promised ROI across local IT stacks and in the cloud.

The challenge? Getting adequate insight into an increasingly complex and dynamic landscape. Between multicloud environments, container-based architecture, and on-premises infrastructure running everything from the latest open-source technologies to legacy software, achieving situational awareness of your IT environment is getting harder to achieve. Many organizations respond by adding a proliferation of infrastructure monitoring tools, which in many cases, just adds to the noise.

To keep pace with innovation and deliver great user experiences at ever-increasing rates of reliability, speed, and scale, IT operations (ITOps) teams need to mature their approach to infrastructure monitoring.

Why ITOps needs to work smarter, not harder

Cloud services, mobile applications, and microservices-based application environments offer unparalleled flexibility for developers and users. The Cloud Native Computing Foundation (CNCF) paints a fast-growing landscape of nearly 1000 cloud-native technologies, and most organizations use many of them. However, this variety and flexibility also create numerous complexity concerns for ITOps teams. The result is a production paradox: with each new cloud service, container environment, and open-source solution, the number of technologies and dependencies increases, which makes it more difficult for ITOps teams to actively monitor systems at scale and address performance problems as they emerge.

To get ahead of this ever-expanding diversity and complexity, ITOps teams need to work smarter, not harder. Leveraging artificial intelligence and continuous automation is the most promising path—to evolve from ITOps to AIOps. Artificial intelligence for IT operations (AIOps) is the discipline of applying artificial intelligence, typically machine learning and pattern recognition—or in the case of Dynatrace, deterministic, causation-based AI—to perform and automate tasks an ITOps team normally performs manually.

Achieving AIOps may seem daunting, but with some planning, teams can implement this evolution in three phases: 1. Evaluate monitoring maturity and goals; 2. Automate infrastructure monitoring; 3. Integrate monitoring on a single AIOps platform.

1. Evaluate infrastructure monitoring maturity and goals

Effective monitoring and diagnostics starts with availability monitoring. But as Gartner notes, most organizations are on a journey to increase both the maturity of their monitoring and its impact. Gartner defines five maturity stages organizations can use to evaluate where they are and where they want to go:

Stage 1: Availability monitoring

This stage is defined by the question “is it up?” and focuses on the ability to collect and correlate events to assess the availability of key services. While this basic information is essential, it provides no insight into the root causes of anomalies or the specific remedies required to resolve, let alone prevent, disruptions.

Stage 2: Service monitoring

The service monitoring stage digs deeper to ask, “is it working?” Here, collecting metrics and monitoring performance help evaluate the efficacy of services rather than simply identifying their state.

Stage 3: Diagnostics

Stage three tackles a bigger question: “What’s the problem?” Dependency mapping, distributed tracing and root-cause analysis (RCA) operations all play a role in identifying what’s gone wrong, why, and what’s required to fix it.

Stage 4: Business insights

With basic operational needs well in hand, stage four focuses on customer journeys and KPIs to answer key questions: How is the performance of the monitored apps and services affecting end users? And what impact is this having on business?

Stage 5: Self-driving solutions

The final stage defines continual optimization and improvement through intelligent automation. This stage of maturity integrates the goals of the previous stages through automation and AI-assisted observability—automatic instrumentation and baselining of dynamic systems, reliable root cause analysis, and auto-remediation workflows—to enhance operational outcomes and prevent issues that can cause disruption.

Worth noting? Recent survey data from Gartner found that half the companies surveyed identified their monitoring operations as stage 2 or below, indicating that for most, monitoring maturity is a work in progress.

2. Automate infrastructure monitoring

Armed with an understanding of their monitoring maturity, organizations can develop a strategy for harnessing their data to automate more of their operations. Such a strategy relies on the ability to implement three capabilities:

End-to-end observability across a broad spectrum of technologies

The first requirement toward automating monitoring is comprehensive observability across the network. This includes all infrastructure layers (networks, hosts, virtualizations, and so on); cloud-native and on-premises environments (including mainframes); and open-source observability frameworks, such as StatsD, Telegraf, Prometheus, and OpenTelemetry.

Automation at every stage of the software delivery life cycle (SDLC)

Automating the entire data life cycle requires the ability to instrument tens of thousands of hosts, including highly dynamic and ephemeral function as a service (FaaS) components. It also requires performance baselining for anomaly detection and precise root-cause analysis, which relies not only on metrics, logs, and traces, but the context of each transaction, including user data.

Automated problem resolution

Automating proactive problem resolution requires intelligence, system optimization, and user experience data to facilitate an understanding of the context and business impact of each transaction. To deliver precise root-cause analysis, AI should be a core component of the monitoring solution. To trigger remediation workflows automatically, system health and anomaly data must be precise and reliable.

3. Integrate infrastructure monitoring on a single AIOps platform

As organizations evolve to embrace Kubernetes and cloud-native architectures, many have also adopted a host of monitoring point solutions—specialized monitoring tools that capture specific metrics of their application environment. With telemetry from many different sources, it can be difficult for teams to have a holistic view of their apps and dependencies. A single integrated observability platform can enable collaboration among operations, development, security, and business teams so they can easily coordinate and make decisions and automate more processes based on the same data.

This platform approach does not require that teams eliminate their point solutions. Rather for teams to adopt a single platform that can ingest data from any point solution in any environment, from legacy software in on-premises infrastructure to microservices-based apps in cloud environments. As different teams have implemented point-solution infrastructure monitoring tools across different disciplines, having a single platform to integrate their data and its context enables them to gain new insights so they can spend more time innovating.

Out-of-the-box AIOps

As organizations adopt more technologies in their evolving multicloud networks, infrastructure monitoring has become much more complex. To keep up with the demands on IT teams, deliver on customer expectations, and achieve business goals, digital teams must also transform how they work. Key to this transformation is adopting end-to-end observability across a broad spectrum of technologies, automating every stage of the SDLC, and automating problem resolution. Adopting an integrated observability platform like Dynatrace enables collaboration among operations, development, security, and business teams.

The Dynatrace Software Intelligence platform is a self-driving AIOps solution that delivers automation and AI-assisted observability for infrastructure monitoring, applications and microservices, application security, digital experience, and business analytics use cases in hybrid and multicloud environments.

The Dynatrace deterministic AI engine, Davis, automatically serves up precise answers, prioritized by business impact. To help organizations achieve AIOps out-of-the-box, Dynatrace combines automatic instrumentation and baselining of dynamic systems with automatic root cause analysis and auto-remediation workflows. Instead of infrastructure monitoring tool sprawl, Dynatrace’s platform approach integrates data from disparate monitoring point solutions, enabling teams to easily coordinate and automate responses.

The post Infrastructure monitoring tools: 3 steps to evolve ITOps into AIOps appeared first on Dynatrace news.

According to Gartner, “the long-term impact of AIOps on IT operations will be transformative.”

AIOps will have a long-term and transformative impact on IT operations. The research firm predicts a significant uptick in AIOps investments over the next two years as organizations look for ways to improve IT outcomes, without breaking budgets or overworking technology staff.

The challenge? While some AIOps tools offer significant benefits over manual processes, not all of them can deliver the results organizations expect. To recognize both immediate and long-term benefits, organizations must deploy intelligent solutions that can unify management, streamline operations, and reduce overall complexity.

Here’s how.

What is AIOps and what are the challenges?

Artificial intelligence operations (AIOps) is an approach to software operations that combines AI-based algorithms with data analytics to automate key tasks and suggest solutions for common IT issues, such as unexpected downtime or unauthorized data access. In practice, AI-driven solutions help reduce the burden on IT teams by allowing them to offload routine monitoring and management tasks so they can focus on mission-critical concerns.

What challenges do AIOps tools address?

Consider data from our recent 2020 Global CIO Report, which found that 86% of organizations are now using cloud-native technologies and orchestration platforms such as microservices, containers, and Kubernetes to meet growing expectations from stakeholders, customers and employees. Despite all the benefits of modern cloud architectures, 63% of CIOs surveyed said the complexity of these environments has surpassed human ability to manage. To tame this complexity, organizations now use an average of 10 different monitoring tools. Despite these investments, these organizations have complete visibility into just 11% of the applications and infrastructure in their environments.

AIOps solutions offer the potential to increase observability, automate processes, and enhance value at scale.

Choosing the right AIOps tools for your needs

As reported by Forbes, AIOps is “moving from marketing hype to a useful tool being adopted across the enterprise.” While broader business deployment stems from increasingly sophisticated AI algorithms and the growing speed at which they’re able to discover new data relationships, it’s also a recognition of a new IT reality: AIOps is here to stay and improving quickly.

But not all approaches to AI are the same, and some are more effective than others for AIOps in modern environments.

The traditional machine learning approach relies on statistics to compile metrics and events and produce a set of correlated alerts. While this statistics-based approach can find and prioritize many alerts, it still relies on humans to analyze the output and determine the root cause of any anomalies or errors. It takes times to train statistics-based machine learning solutions, and this approach doesn’t scale easily with modern, dynamic cloud-native environments.

Another approach is deterministic AI, which uses systematic fault-tree analysis to immediately determine the root cause of a problem. This approach instantly detects anomalies — a service responding slowly, for example — and examines all its dependencies and all their dependencies (and so on) to pinpoint exactly what’s happening and where, in real time. Accompanied by an easily visualized map of the original malfunction’s route through all downstream processes, the deterministic AI approach can find the exact point the problem was triggered and its downstream effects, so analysts can focus on implementing solutions, automating responses, and developing new innovations.

As DevOps teams evaluate what AIOps solution to adopt, it’s important to know exactly what each does and how, since they don’t all provide the same level of autonomous insight.

What are the benefits of AIOps tools?

In theory, implementing an AIOps solution across enterprise IT environments can improve efficiency, drive better overall business value, and improve customer success. But what does this look like in practice?

Effectively deployed, potential benefits of AIOps initiatives include:

Improved alert management

Many IT teams now suffer from “alert fatigue” as the volume, velocity, and variety of alerts increase exponentially in multicloud environments. As noted by CDO Trends, however, effective AIOps implementation can help reduce false alarms by up to 90% and reduce the impact of redundant or irrelevant notifications.

Enhanced event prioritization

Which alerts demand priority response, and which can wait? The sheer volume of data sources and potential security concerns makes this challenging for any IT team — add in multiple cloud environments and open-source resources, and effective management becomes almost impossible. AIOps solutions that use advanced algorithms based on fault-tree analysis can immediately identify the alerts that matter so teams can respond rapidly, automate more processes, and stay focused on what drives the business.

Reduced IT spend

According to our research, IT and cloud operations teams spend 44% of their time just “keeping the lights on” — ensuring tools and technologies work as expected and on-demand. By applying automated, AIOps tools, however, companies could save an average of $4.8 million each year.

Streamlined digital transformation

Digital transformation now drives business success, but only if organizations can manage the complexity of their modern environments with a common language and a single source of truth for all aspects of digital performance. Here, AIOps tools can help companies accelerate their digital transformation by consuming and analyzing the ever-increasing amount, diversity, and velocity of data in their multi-cloud environments, and applying AI analytics to streamline and automate their operations workflows.

What is the impact of AIOps on the business?

While AIOps tools offer a host of potential benefits for IT teams, the biggest practical impact for organizations can be summed up simply: reducing the need for humans to perform manual tasks.

Here’s why it matters: for many businesses, increasing investments in cloud and mobile technologies are critical to retaining a competitive market edge at the cost of increased management complexity for IT teams. Standard management strategy is to expand the number of monitoring tools in use, which boosts the amount of actionable data available, but also puts IT teams in the unenviable position of trying to sort through disparate reports, alerts, and recommendations manually. This is a tough task for even large, experienced teams at the best of times, and almost impossible with the increasing use of third-party and open-source tools and technologies.

AIOps solutions offer a way to deliver on IT operations priorities such as complete observability across multi-cloud environments, accurate and reliable business metrics, and prioritized alerting that reduces the need for manual intervention.

Streamlining Success: a single AIOps platform

Despite the increasing maturity and availability of AI-driven tools, just 19% of operational processes for digital experience management and observability have been automated on average. So, what’s holding companies back? Forty-eight percent of CIOs point to lack of internal technical skill, while 43% highlight the absence of a common data model to enable accurate and consistent AI decisions. Furthermore, 42% percent cite the lack of existing frameworks to effectively implement automation.

To break through this barrier to automation, organizations need a single source of software intelligence they can rely on. AIOps tools that use statistical, correlation-based machine learning can’t scale with ever-increasing IT complexity. That’s why we built the Dynatrace platform to use deterministic AI with fault-tree analysis, which can provide immediate and accurate answers with no guessing or time-consuming model training.

Automatic, end-to-end observability of the entire software stack from a single source provides automated insights teams can use to automate and streamline operations so they can focus their time on solving issues that matter.

The post From AIOps tools to an AIOps platform: what it takes to automate AI operations appeared first on Dynatrace news.