What is OpenShift?

Red Hat OpenShift is a cloud-based Kubernetes platform that helps developers build applications. It offers automated installation, upgrades, and life cycle management throughout the container stack — the operating system, Kubernetes and cluster services, and applications — on any cloud. OpenShift gives organizations the ability to build, deploy, and scale applications faster both on-premises and in the cloud. It also protects your development infrastructure at scale with enterprise-grade security.

Self-hosted Kubernetes installations or services — such as Amazon EKS, Azure Kubernetes Service, or the Google Kubernetes Engine — make it possible for enterprises to select and implement best-fit functions. But OpenShift provides comprehensive multi-tenancy features, advanced security and monitoring, integrated storage, and CI/CD pipeline management right out of the box.

The result? For organizations looking to transform and modernize, OpenShift allows you to scale so you can grow your business through cloud-native development. OpenShift and Kubernetes can simplify access to underlying infrastructure and help manage the application lifecycle and development workflows.

Read on for a deeper look into Red Hat’s Kubernetes platform, what it can do for you, and the benefits of OpenShift automation.

OpenShift key features and benefits

OpenShift 3.0 is built on the most popular Linux container technology, Docker. Since its inception in 2013, Docker has become the de facto standard for developers due to its ability to make more efficient use of system resources, ship software faster, and minimize security issues.

While still supporting Docker containers, OpenShift 4.0 defaults to the Container Runtime Interface — Open Container Initiative (CRI-OCI), which can handle a greater number of container nodes at scale.

OpenShift also leverages Kubernetes, which allows for the orchestration of many containers and scaling running in the cloud. Built on these powerful technologies, OpenShift offers key benefits for software development and container deployment:

• Run cloud-native microservices at scale. Microservices architecture combines loosely-coupled functions to create high-performing applications. The small size of these services makes them highly testable and easy to maintain while simultaneously increasing flexibility in how apps are structured and deployed. OpenShift makes it possible to create and deploy cloud-native microservices at scale, either to enhance existing application performance or build entirely new apps from the ground up.
• Scale and manage infrastructure. The flexible, cloud-based nature of OpenShift gives companies complete control over the scale and management of their application infrastructure. By removing concerns around storage, security, and lifecycle management, businesses can instead focus on application development, support, and evolution.
• Access a wide ecosystem of partners with open-source and cloud-native technologies. OpenShift also contains a set of developer tools in the form of command-line utilities and IDE support that allow developers to write and deploy to production with increased velocity. Additionally, OpenShift includes a set of pre-created, easy-to-use templates that offer a simple click interface and can be easily customized to meet your needs. It also works with popular continuous integration tools such as Jenkins.

OpenShift, DevOps, and CI/CD

The emergence of DevOps and continuous integration (CI) and continuous development (CD) pipelines has fundamentally changed the nature of software design, development, and deployment. By continually evaluating applications across multiple operational or development vectors, organizations can avoid common pitfalls and reduce project timelines — research shows that high-performing DevOps teams have 46 times more frequent code deployments and 440 times faster lead time from commitment to deployment.

OpenShift empowers easy integration with leading CI/CD platforms, AI-powered performance monitoring solutions, and user-demand analysis tools. By taking better control of their Kubernetes environments, OpenShift can help organizations reach their full DevOps potential.

How OpenShift enhances Kubernetes

Kubernetes allows for the scheduling of containers on worker nodes and provides container orchestration, load balancing, distribution, and scalability. OpenShift takes these concepts and enhances them.

And while security vulnerabilities can pose a critical threat to your business, OpenShift allows updates and security patches to be deployed to an entire cluster with a single click. Because it’s based on RHEL CoreOS, OpenShift can also update the underlying operating system the nodes are running on.

Along with security and developer convenience features, OpenShift and Kubernetes integration takes scalability to a further level. As it manages both the underlying operating system and the Kubernetes plane, the entire cloud infrastructure can be scaled up or down based on your needs. This fine-grained control allows OpenShift 4 to scale from 10 to 10,000 containers.

The latest version of OpenShift also makes for a smarter Kubernetes platform by streamlining the installation and upgrade experience. From start to finish, developers can now be up and running on a Kubernetes cluster in 15 minutes — including full tooling of metering and monitoring.

Benefits of using OpenShift to manage Kubernetes

OpenShift provides a variety of benefits over managing Kubernetes on your own infrastructure. Though Kubernetes offers many tools to help manage workloads, it can be notoriously difficult to manage at times due to its complexity. OpenShift eases the burden of Kubernetes several ways:

• Simplifies Kubernetes management tools. OpenShift incorporates all the tools necessary to manage the underlying nodes and control plane.
• Enhances DevOps productivity. By making it easier and faster to set up and manage Kubernetes, OpenShift enhances developer productivity by simplifying CI/CD processes.
• Speeds up development. Streamlining and consolidating Kubernetes setup and management reduces the time to working microservices. This in turn reduces the burden and overhead on the DevOps team and allows development teams—and users–to utilize distributed microservices faster.

Key metrics for OpenShift monitoring

Monitoring is an important part of maintaining any Kubernetes environment. Here are some key metrics for monitoring an OpenShift cluster to understand how it’s performing:

• Number and state of pods and nodes. For a given setup of objects in Kubernetes, it’s important to understand their state and health at any given time. These metrics can tell you the condition of the underlying nodes and the number of pods running in each node. Each one needs to have monitoring and alerting in production. Specific metrics to measure include current pods, available pods, unavailable pods, and current node conditions. It’s also important to monitor for hotspots in your application — that is, nodes that are over-burdened on resources while others are sitting idle.
• Resource usage. Resource metrics give an insight into the memory, compute, and disk utilization of the underlying application. These metrics help you understand the provisioning of the cluster as well as the load on the total application. Key metrics here include memory requests, CPU requests, disk utilization, and network throughput.
• Control plane performance. The Kubernetes control plane metrics give insight into the performance of Kubernetes itself. These metrics tell you how many times resources have been requested, the total amount of time spent waiting for a queue, or the total number of attempts to schedule a pod. Common control plane metrics include the total count of API requests, the sum of request durations, and the total amount of time spent processing specific items in work queues.

Teams often overlook alerting on these metrics, focusing only on application metrics, but monitoring Kubernetes performance will give you a holistic view into your OpenShift cluster.

OpenShift Best Practices

Follow these best practices to ensure that OpenShift is used efficiently and effectively and that your environment stays secure and reliable.

1. Use projects and namespaces.

Projects and namespaces are a way to organize your OpenShift resources. Projects are the top-level organizational unit, and namespaces are a way to subdivide projects further. They can help keep your OpenShift environment organized and manageable.

2. Limit container runtime privileges.

Containers should only have the privileges that they need to run. This can help to improve security and prevent containers from gaining unauthorized access to resources.

3. Monitor and analyze audit logs.

Audit logs can provide valuable insights into what is happening in your OpenShift environment. Monitor and analyze audit logs to ensure your applications run as expected and identify potential security issues.

4. Secure etcd.

Etcd is a key-value store that stores important data about your OpenShift cluster. It is essential to secure etcd to prevent unauthorized access to this data.

5. Use the OpenShift Container Security Operator.

The OpenShift Container Security Operator can help you improve your OpenShift environment’s security. It can automatically apply security policies to your containers and help you to identify and remediate security vulnerabilities.

Additional best practices:

• Use the latest version of OpenShift.
• Use a separate build image and runtime image.
• Stick to the restricted security context constraint where possible.
• Protect the communication between application components using TLS.
• Use a load balancer to distribute traffic to your applications.
• Back up your OpenShift environment regularly.

Use Cases for OpenShift

Let’s examine some of the most convincing use cases for OpenShift and explore how it can transform businesses’ operations.

• Deploying and managing cloud-native applications: OpenShift is a native Kubernetes platform, making it ideal for deploying and managing cloud-native applications. Cloud-native applications are built using microservices architecture, which makes them highly scalable and resilient.
• Modernizing legacy applications: OpenShift can also be used to modernize legacy applications. By containerizing legacy applications, they can be made more portable and scalable.

• Running mission-critical applications: OpenShift can be used to run mission-critical applications. The platform’s built-in security features and high availability capabilities make it ideal for running applications that can’t afford to go down.
• Building and deploying data-intensive applications: The platform’s support for various data storage options makes it easy to build and deploy data-intensive applications.
• Running edge computing applications: Edge computing applications are deployed at the edge of the network, closer to the end users. This can improve performance and reduce latency for applications requiring real-time data access.

Additional use cases:

• DevOps: Improve the DevOps process by providing a centralized platform for building, deploying, and managing applications.
• Testing: Create a staging environment for testing applications before they’re deployed to production.
• Training: Train developers to build and deploy containerized applications.

By using OpenShift, organizations can improve the speed, agility, and security of their application delivery process.

OpenShift automation

OpenShift is a great platform to use for building and shipping cloud-native applications. However, with dynamic containers running in microservices, increasingly frequent code pushes, and several levels of abstraction distancing you from the cloud infrastructure, how can you know how your applications are performing at any given time? The answer lies in automation.

Automation has become an essential part of many organizations’ overall digital transformation strategies for cloud-native environments. But why is automation so important, and how is it relevant to OpenShift? To understand that we first need to take a deeper look into containers.

Containers can be challenging to monitor because they’re always changing. As organizations shift from monolithic infrastructures to microservices, the number of containers supporting these microservices can explode over time to hundreds and thousands of nodes.

With these dynamic environments, cluster performance and container health are only part of the puzzle. Knowing the health of your container, while important, doesn’t give you the complete picture, especially for self-managed OpenShift Container Platform (OCP) operators. You also need to know the health of the underlying control plane, the services running in the containers, and their overall impact on application health.

Manually installing different agent types or collecting and correlating metrics is simply ineffective. That’s why automation is critical — it solves the challenge of making sense of dependencies within the context of the entire technology stack to understand the impact on users and prevent business-impacting issues.

Of course, not all automation is created equal. For example, you may get a dashboard view that falls short in dynamic environments. But automated monitoring of cloud-native workloads, microservices, and containers can help you understand all relationships and dependencies across the cloud stack.

How Dynatrace makes monitoring OpenShift easy

The Dynatrace Software Intelligence Platform provides answers powered by the causal AI engine, Davis® and scales to the largest and most complex environments. Dynatrace can monitor and orchestrate applications, clusters, and underlying cloud infrastructure in OpenShift. Dynatrace automates your entire hybrid multi-cloud ecosystem and provides a full-stack overview of your cloud environment, from the underlying infrastructure up to the end user.

One of the core value propositions of the Dynatrace and OpenShift technologies is accelerating digital transformation by empowering your DevOps team. With Dynatrace’s automatic, zero-configuration monitoring, alerting, and observability solution, your DevOps team can take advantage of these tools with zero code overhead. There’s no need to change any application code, as Dynatrace automatically allows you to deliver more precise, AI-powered answers to dramatically simplify Kubernetes roll-out and management.

In addition, Dynatrace AI continuously maintains a complete real-time topology and dependency map of your environment—Smartscape—so you can always see the health of your applications, the health of your container processes, and the health of your code running inside — all in context to each other. Dynatrace has simplified this process through the OneAgent Operator, which uses Kubernetes-native means to roll out OneAgent to OpenShift nodes. Once deployed, Dynatrace OneAgent automatically injects the OneAgent into applications containers with automated visibility into CRI-O containers and traces.

What is OpenShift? And how can you leverage AI and automation to make it easy? To learn more about Dynatrace and Red Hat OpenShift, check out this blog from Red Hat, The Power of OpenShift, The Visibility of Dynatrace.

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As organizations adopt more cloud-native technologies and IT infrastructure is becoming increasingly distributed, organizations must align business objectives and end-user experience with the availability and performance of the IT infrastructure. This shift requires infrastructure monitoring to ensure all your components work together across cloud environments, operating systems, storage, servers, virtualized systems, and more.

What is infrastructure monitoring, then, and why does it matter more than ever for cloud-native architectures? Here’s an introduction to infrastructure monitoring and the business value it delivers, and some best practices to keep in mind when implementing it within your organization.

What is infrastructure monitoring?

Infrastructure monitoring is the process of collecting and analyzing data from IT infrastructure, systems, and processes and using that data to improve business outcomes and drive value across the whole organization.

Simply put, infrastructure monitoring is the oxygen to your infrastructure. It collects all the data needed to provide a complete picture of availability, performance, and resource efficiency so your applications and services remain up and available to your users.

As businesses rely more heavily on applications and services for their revenue streams, system performance has become mission-critical. Millions of people worldwide have recently put a massive strain on these organizations’ digital properties — from unemployment claims and small business loans to telehealth and grocery shopping. The associated applications and services are mission-critical for individuals and organizations alike.

Infrastructure monitoring ensures organizations can respond to issues proactively, preventing loss of time and money. This makes infrastructure monitoring the essence of mission-critical, delivering these key capabilities:

• The ability to optimize business requirements and user experience
• The flexibility and scalability to ingest data from a variety of sources and to handle planned and unplanned traffic spikes
• The ability to detect and alert on outages, resource utilization, and performance degradations to minimize downtime and increase operational efficiency
• Pinpoint root causes to determine precisely where a problem originates in the infrastructure or application
• The ability to drill down into specific faulty infrastructure components and trigger remediation

How Infrastructure Monitoring Works

Infrastructure monitoring typically involves the following steps:

• Data collection: Infrastructure monitoring solutions collect data from a variety of sources, including:
  • Operating systems
  • Hypervisors
  • Containers
  • Databases
  • Network devices
  • Applications
  • Logs
  • Metrics
• Data analysis: Once the data has been collected, it is analyzed to identify trends and patterns. This can be done using a variety of tools and techniques, such as filtering, querying, statistical analysis, machine learning, and anomaly detection.
• Alerting: When the data analysis identifies a potential problem, the monitoring system generates an alert. In addition to being presented in a dashboard, alerts can also be sent to IT staff via email, SMS, or other messaging channels, like Slack or Microsoft Teams.
• Remediation: Once an alert has been received, IT staff can investigate the problem and take steps to resolve it. In more advanced practices, the alert can trigger an automation and open, or update, a ticket with an IT service management (ITSM) solution.

There are two main types of infrastructure monitoring:

1. Agent-based monitoring: Agent-based monitoring involves installing a software agent on each system that needs to be monitored. The agent collects data from the system and sends it to the monitoring server. This can include, for example, all requests to an application, service, or host.
2. Agentless monitoring: Agentless monitoring does not require the installation of any software agents. Instead, it uses protocols such as SSH, SNMP, and WMI to collect and forward data from systems via exposed remote APIs. Though there can be advantages to this approach, the depth of agentless monitoring is severely reduced.

Challenges of infrastructure monitoring in cloud environments

Modern infrastructure demands modern monitoring, especially when you’re negotiating the unique challenges of multicloud environments, which don’t always enable the level of visibility needed into cloud infrastructure. Monitoring these environments is tricky since each cloud vendor offers native monitoring tools. Juggling multiple monitoring solutions makes it hard for organizations to gain a comprehensive view of their cloud infrastructure.

Your DevOps team may also be struggling with the classic “cattle versus pets” transition to an operational service model where computing assets become an interchangeable resource instead of something unique and irreplaceable. As organizations adopt microservices-based architecture and cloud-native technologies, they are also looking to adopt best practices for managing their new cloud infrastructure so they can provide better service and minimize unplanned downtime.

Infrastructure monitoring provides critical intelligence to help you optimize the health and utilization of these cloud-native environments, ensuring more reliable performance and a high-quality user experience.

What are the benefits of infrastructure monitoring?

Organizations can’t afford to wait for alerts to come in when a system or application component has failed, especially if they plan to honor end-user service level agreements (SLAs). Instead, they need to adopt a proactive posture to identify and resolve potential infrastructure issues before they impact the user experience. Infrastructure monitoring helps organizations achieve this goal by accelerating root cause analysis, which empowers cross-functional teams to effectively collaborate and fix problems before they flare into five-alarm fires.

Infrastructure monitoring also helps organizations analyze performance trends continuously so they can better understand what peak performance looks like, optimize performance where appropriate, and flag potential issues well in advance.

DevOps teams can even leverage infrastructure monitoring as part of their A/B testing experiments. This way, teams can determine upfront how certain features or enhancements will impact application performance down the road. DevOps teams can also utilize infrastructure monitoring to validate deployments.

Your ITOps and SRE teams can also leverage infrastructure monitoring, taking advantage of the automation capabilities in modern infrastructure monitoring tools to achieve end-to-end observability across your entire infrastructure. In doing so, you can consistently deliver on customer expectations, and enable your organization to achieve its most ambitious goals as it continues to scale for growth.

What observability data should you use?

Your infrastructure monitoring will only deliver high-quality insights if you have the right tools with access to the right information. With that in mind, include these types of observability data sources:

• Metrics: Quantitative data is especially useful for creating visualizations and identifying patterns in performance over time. Values represented as counts or measures calculated or aggregated over a time period deliver crucial information for performance and state-based analysis.
• Event logs: Every system and service generates event logs, which can give you insights into what’s happening and aid in troubleshooting.
• Distributed traces: For better insight into how various aspects of your environment interact with one another, capture distributed traces to record the journeys of specific transactions as they make their way through your infrastructure.
• Metadata: Additional information, such as topology details, name spaces, and priority data, will help you understand the significance and impact of events as they interact with other components of your infrastructure.
• UX data: A view into how users are experiencing your site or applications is one of the most important dimensions to understand how your infrastructure is performing. User experience data, such as page load times and latency, will give you better insight into your UX in real-time and empower you to adjust the UX if needed .
• Open-source telemetry: There are many open-source options designed to help you achieve better observability across your entire environment. These industry-standard tools include OpenTelemetry , Prometheus, and StatsD, to name a few.
• Cloud integrations: Modern infrastructure includes cloud infrastructure, which is why cloud integrations, such as CloudWatch for Amazon Web Services (AWS), can be helpful sources of observability data for infrastructure monitoring.

Infrastructure monitoring best practices

Following a few best practices will help you get the most value from your infrastructure monitoring program. For example:

• Leverage automation: Augment your capabilities with infrastructure monitoring tools that feature automation. This will help you gain complete end-to-end observability across the full stack and transition to AIOps for infrastructure monitoring.
• Configure comprehensive alerts: When your alerts are specific, they’re less likely to result in false positives. Comprehensive alerts that provide a certain level of redundancy will be more likely to deliver the heads-up you need in any situation.
• Prioritize alerts: Organize and prioritize notifications so you don’t miss the most important alerts — particularly the ones reporting impacts to the user experience.
• Create role-specific dashboards: Infrastructure monitoring tools allow you to create a range of custom dashboards that various teams within your organization will find useful when monitoring KPIs of importance to them. Set up dashboards for your ITOps teams, your security teams, and business leaders so everyone has access to the insights they need at a glance.
• Do a test run: As with any mission-critical system, you’ll want to make sure your infrastructure monitoring tools are working as expected before you begin to rely on them on a day-to-day basis. Schedule a test run and make sure everything is running according to plan.
• Regularly review metrics: As your business goals change and your infrastructure evolves, so will the metrics and KPIs you need to track. Review them at regular intervals so you don’t unintentionally develop any blind spots across your infrastructure.
• Tap your vendor’s expertise: Struggling to fine-tune or optimize your infrastructure monitoring as your organization digitally transforms? Take full advantage of your infrastructure monitoring vendor’s expertise. They’ve overseen infrastructure monitoring deployments across countless organizations and can help you achieve your monitoring goals faster.

Infrastructure monitoring use cases

Infrastructure monitoring is critical to modern IT operations. Organizations can proactively identify and resolve potential issues before they cause downtime or performance problems, ensuring IT infrastructure operates at peak efficiency.

The most common infrastructure monitoring use cases include the following:

1. Detecting and resolving network issues. By monitoring network traffic and other metrics, infrastructure monitoring tools can recognize bottlenecks that affect network performance. This information can help network teams identify the root cause of issues and take corrective action before they affect end users.
2. Ensuring compliance and security. Organizations can detect potential security threats and ensure their infrastructure complies with relevant regulations and standards.
3. Tracking server health and utilization. These tools can provide real-time insights into the health and utilization of servers, including CPU usage, memory utilization, and disk space. This enables organizations to identify potential issues with server capacity and ensure application performance isn’t affected.
4. Capacity planning and optimization. Organizations can identify areas where additional resources may be needed to make informed decisions about how to allocate resources for maximum efficiency involving capacity planning and optimization.
5. Monitoring application performance. By monitoring metrics such as response time, transaction volume, and error rates, organizations can detect issues with application performance and resolve them to avoid further complications.

What to look for when selecting an infrastructure monitoring solution

With organizations focusing on modern infrastructure, traditional infrastructure monitoring tools are not going to cut it. An all-in-one approach to monitoring cloud platforms and supporting applications and infrastructure is the best approach. Here are some key attributes to look for:

• All-in-one platform: Break down silos between apps and infrastructure teams with end-to-end visibility across the entire IT stack.
• AI-assistance: Use AI to detect anomalies and benchmark your system. This will allow your IT team to focus on what matters: proactive action, innovation, and business results.
• Contextual information: Go beyond metrics, logs, and traces with UX and topology data to understand billions of interdependencies.
• Root-cause analysis: Get actionable answers to problems in real-time, down to the code level.
• Automation for large-scale dynamic environments: This includes discovery, instrumentation, baselining, agent life-cycle management, and problem analysis.
• End-to-end coverage for hybrid cloud: Gain comprehensive support for multi-cloud, third-party integrations — including public cloud, on-prem virtualization, mainframes, database vendors, etc.
• Cloud-native architectures: These provide support for containers and serverless, including open standard, such as OpenTelemetry, Prometheus, StatsD, and Telegraf.

All-in-one infrastructure monitoring for modern cloud environments

Now more than ever, software needs to run perfectly, and your IT infrastructure should be thought out strategically in the context of the end-user and business. Observability into performance is crucial across the IT stack and can only be achieved through a smart investment in infrastructure monitoring. This helps ensure your organization has a comprehensive look at performance and availability across your entire IT ecosystem.

Dynatrace provides a single-interface platform that delivers all-in-one infrastructure monitoring and end-to-end observability across today’s modern environments — including hybrid cloud and cloud-native architectures, all with contextual insights and precise, AI-driven answers.

With a single source of truth and precise root-cause analysis, your DevOps teams can collaborate more effectively and waste less time. And with observability and automation at scale encompassing your infrastructure, your SREs can evolve ITOps into AIOps, creating valuable capacity to innovate, optimize user experiences, and transform faster.

Infrastructure monitoring should be at the core of any organization’s IT strategy. That’s exactly what Docebo, one of the largest providers of online learning platforms, realized when they decided to use infrastructure monitoring to gain visibility across their entire infrastructure.

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In September, we announced the availability of the Dynatrace Software Intelligence Platform on Microsoft Azure as a SaaS solution and natively in the Azure portal. Today, we are excited to provide an update that Dynatrace SaaS on Azure is now generally available (GA) to the public through Dynatrace sales channels.

Organizations are continuing to prioritize digital transformation as they race to keep up with evolving customer demands. Dynatrace SaaS availability on Azure helps the world’s largest organizations achieve this through enabling faster cloud adoption and more effective digital transformation. With this solution, customers will be able to use Dynatrace’s deep observability, advanced AIOps capabilities, and application security to all applications, services, and infrastructure, out-of-the-box. This enables organizations to tame cloud complexity, minimize risk, and reduce manual effort so teams can focus on driving innovation.

Additional benefits of Dynatrace SaaS on Azure include:

• No infrastructure investment: Dynatrace manages the infrastructure for you, including automatic visibility, problem detection, and smart alerting across virtual networks, virtual infrastructure, and container orchestration. This means you no longer have to procure new hardware, which can be a time-consuming and expensive process. Additionally, the investment for on-prem installations is a lot more expensive to manage over time.
• No operational duties: Dynatrace operates the product for you with auto-discovery of your entire stack, end-to-end, including processes running inside containers. Upgrades are automatic as well – you can control when they happen and Dynatrace does the rest. If any issues do arise on your SaaS tenant, Dynatrace is the first to respond and proactively prevent issues before the customer notices.
• Security: Data is stored securely in the Dynatrace cloud (powered by Azure). Dynatrace captures all your data, including host and application metrics, basic-network metrics, real-user metrics, mobile metrics, cloud-infrastructure metrics, log metrics, and much more. As such data may contain private or sensitive user information, Dynatrace offers data masking features to assist in complying with data privacy and data protection obligations. All data at rest is stored in Azure Storage and is encrypted and decrypted using 256-bit AES encryption (FIPS 140-2 compliant).
• SaaS-first innovation: Innovative product updates and features are released to SaaS first and then released to managed deployments later. At Dynatrace, we innovate faster and schedule releases more frequently on SaaS vs. our Managed deployments.
• Scalability: Dynatrace provides easy and limitless horizontal scalability for SaaS deployments., and scales up as monitoring environments grow by simply adding nodes, with built-in failover and automatic load balancing to ensure optimal resource usage.
• Reduced time-to-benefit: A traditional on-prem deployment model is time-consuming. You must procure hardware, install the OS on the server, install the application, and configure it. With SaaS, the applications and server now reside in the cloud on our Azure tenant. With a push of a button, Dynatrace automatically provisions and deploys your SaaS tenant on-demand in Azure. This provides major time savings, which teams can spend on other business priorities and initiatives.
• Azure Region – Dynatrace SaaS on Azure will initially be available in two Azure regions: Azure US East (Virginia) and Azure EU West (Netherlands). More Azure regions will be brought online over time as customer demand follows.

Dynatrace is excited about our strategic partnership with Microsoft and the technology integrations and advancements we have made jointly over the past few years, as well as new ones to be announced. Check out Dynatrace’s listing in the Azure Marketplace to see how easy it is to get started.

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This past year, we’ve seen organizations rapidly transform and the digital future shows no sign of slowing down. Both Dynatrace and ServiceNow are committed to supporting organizations during this time of transformation.

With the explosion of digital services across every industry, ServiceNow and Dynatrace see three big challenges IT teams are facing as they continue to transform business. These include:

• IT complexity: Hybrid, multicloud, microservices, and containers make it challenging to see where systems are connected and dependent.
• Lack of visibility: This makes it nearly impossible for teams to get to the root cause when manually interpreting billions of event sources.
• User expectations: Lack of insight into the user experience leads to an inability to prioritize those expectations accordingly.

Dynatrace and ServiceNow’s partnership allows organizations to manage this complexity through these three main pillars:

• Simplify complexity: Through automatic tracing of services, applications, topology, and dependencies, with code-level insights for an end-to-end picture of the entire IT estate.
• Complete visibility: Deterministic AI provides real-time views into application and infrastructure problem identification with precise root-cause analysis and business impact.
• Positive user experience: Comprehensive real-user insights enable teams to make proactive and measurable experience improvements before business impact.

Dynatrace and ServiceNow’s strategic integration enables organizations to lean into the new hybrid normal, with integrated platforms designed to eliminate failures, accelerate business outcomes, and perpetuate uninterrupted services.

Dynatrace and ServiceNow integration use cases

In talking to our customers, we’ve learned that some of the main ways they rely on Dynatrace and ServiceNow to deliver enhanced business value include:

• Minimizing enterprise silos and maximizing digital transformation: Infrastructure and application teams must work together to solve any service or infrastructure issue. The Dynatrace and ServiceNow integration assign issues to the right teams, reducing the need for war-rooms and saves valuable time.
• Overseeing the big picture of service infrastructure in real-time: The combined Dynatrace and ServiceNow solution is unique in how it gets to the root cause, with deterministic AI to automatically pinpoint the causation of underlying performance issues, including container-based applications. By capturing and ingesting data from Dynatrace, ServiceNow expands correlation identifiers for affected hosts and provides a single source of truth across the IT landscape.
• Managing change with service infrastructure towards automation: With the addition of Dynatrace’s AI automatic change detection in applications, services, and infrastructure to ServiceNow, users can automatically trigger workflows to execute to remediation of an incident before it can impact the business or end-user.

“ServiceNow is committed to providing the necessary tools for organizations to digitally transform,” said Jeff Hausman, SVP & GM, IT Workflows Operations Management at ServiceNow. “We are pleased to have Dynatrace in our partner ecosystem to increase trust and accelerate time to value for our joint customers as we seek to jointly solve the challenges of transitioning to a hybrid world of work.”

Dynatrace and ServiceNow provide organizations with better decision-making capabilities to accelerate critical business outcomes. Together, this integration delivers the best strategy for intelligent observability, to help organizations meet the challenges that come with the expedited digital transformation.

Dynatrace integrates with ServiceNow in three main ways

• Incident Integration: Dynatrace automatically identifies service health and performance incidents. The Dynatrace Incident Integration app synchronizes these incidents into the ServiceNow® Event Management Dashboard.
• CMDB Integration: Dynatrace pulls in vertical and horizontal relationships between CIs and the metadata through integration with ServiceNow. The new Service Graph Connector for Dynatrace observability integrates into ServiceNow CMDB and existing service maps and can enhance the visibility of your environment by understanding how events services are impacted. This saves teams valuable time and resources, enabling them to focus on higher-value initiatives, including driving digital transformation and delivering seamless experiences to customers.
• Events Integration: Dynatrace pushes events into the ServiceNow Events table, providing complete visibility, including code-level metrics and tracing to proactively reduce service outages.

By tying together deep observability, automated workflows, and configuration data organizations can successfully transform their business.

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