HCL Workload Automation Observability for Datadog

In recent blog posts we’ve discussed the importance of observability in digital business applications and how it has supplanted the concept of monitoring, enabling end-to-end visibility into business applications, systems, APIs, microservices, networks, infrastructure and more. To learn more, see HCL Workload Automation Observability for Splunk.

In addition to the integration with Splunk, HCL Workload Automation is also integrated with another observability tool commonly used by companies: Dynatrace. To learn more, see HCL Workload Automation Observability for Dynatrace.

Let’s take this opportunity to focus in this post on why we chose Datadog as an additional integration, and then see how this integration works.

Gartner has published the 2022 Magic Quadrant for Application Performance Monitoring (APM) and Observability. Datadog has been recognized as a “Leader” within this Magic Quadrant report for the second consecutive year, with the highest position for Ability to Execute. Datadog’s unified approach to APM and observability breaks down organizational silos and creates a fully integrated experience that meets customer needs and solves existing challenges. Whether it’s IT operations, security, or development teams, users also identify Datadog’s AI engine that simplifies monitoring cloud-native architectures as key differentiator. From accelerating root cause analysis to identifying end-user pain points, Datadog supports customers’ day-to-day workflows across every layer of their stack. Datadog is capable of ingesting data from applications like HCL Workload Automation (HWA) and data can be filtered and displayed in dashboards.

HWA Observability Dashboard for Datadog is the solution developed to integrate HWA with Datadog. This integration comes with predefined dashboards which can be deployed on Datadog. In these dashboards, you can view the whole status of your workloads at a glance for the engine you have configured.

The Overview Dashboard provides a single, consolidated view for monitoring the activities, infrastructure, jobs, job-streams, workstations status and so on, and you can drilldown in each dashboard for more complete data insights.

Dashboard: Jobs and Job-Streams

Description: The Jobs and Job-Streams dashboard displays the status and insights of HWA Jobs, Critical jobs, and Job Streams.

Input Source: HWA deployment events sidecar container and HWA server Logs.

Dashboard: KPIs and Workstations

Description: KPIs and Workstations dashboard displays the HWA KPIs information and allows drilldown to see the timeseries data in visual representation for defined KPIs.

Input Source: API exposed by HWA Server.

Dashboard: Activity Monitoring

Description: Activity Monitoring dashboard displays workstations and audit information such as activities performed by users.

Input Source: HWA sidecar containers, DB auditing and Plan auditing.

Dashboard: Infrastructure Monitoring

Description: Infrastructure Monitoring dashboard displays an overview of infrastructure details of HWA deployed on Kubernetes environment.

Input Source: Infrastructure logs exposed by Kubernetes cluster where HWA is deployed.

Conclusion
Among all the observability tools available in the market, HWA is integrating with top tools like Datadog. By leveraging Datadog’s predefined dashboard capability, this integration makes it easy to visualize KPIs, search and filter data among large volumes of logs, and visualize them in dashboards. Check out this demo-video of the HWA Observability Dashboard for Datadog to learn more as you prepare an HWA observability strategy that cuts through all the blind spots and reduces your mean time to issue resolution. Also, you can read about additional HWA integrations with observability tools such as Splunk and Dynatrace.

But it doesn’t end here! Keep following our posts: new integrations with additional observability tools will arrive soon.