When working with observability data, a good portion of it comes in as time series data — things like CPU or memory utilization, network transfer, even application trace data. And the Elastic Stack offers powerful tools within Kibana for time series analysis, including TSVB (formerly Time Series Visual Builder). In this blog post, I’m going to attempt to demystify rates in TSVB by walking through three different types: positive rates, rate of change, and event rates.

Elasticsearch allows you to store, search, and analyze large amounts of structured and unstructured data. This speed, scale, and flexibility makes the Elastic Stack a powerful solution for a wide variety of use cases, like system observability, security (threat hunting and prevention), enterprise search, and more. Because of this flexibility, effectively architecting your deployment’s data storage for scale is incredibly important.

If you’ve followed our latest blog posts, you’ll have learned how to send metric data to Logz.io and visualize that data on Infrastructure Monitoring – our Grafana-based metrics monitoring solution that we made Generally Available on Monday. At this point you’ll have some nice looking Grafana dashboards in your account.

Many mature security teams look to the MITRE ATT&CK® matrix to help improve their understanding of attacker tactics, techniques, and procedures (TTPs) and to better understand their own capabilities relative to these common adversarial approaches. With the release of Elastic Security 7.6, Elastic SIEM saw 92 detection rules for threat hunting and security analytics aligned to ATT&CK.

Sitting here in my home office reflecting the potential problems the world faces both in the short term and longer term, I can’t help but think back to my career before coming to Splunk. That time was spent on the ground working ‘in the real world’, maintaining the operational and security state of systems and networks. I can empathise with the huge pressures the entire IT chain from CIOs, CISOs, IT Managers and IT admins are under right now.

In Grafana, Loki isn’t just for log visualization anymore. Now there’s a simple way to use a Loki datasource as a metric datasource in your graphs. This used to require a hack to make it work – adding Loki as a Prometheus datasource – and the process was very tedious. But Grafana v6.6 integrates Loki even better than before. As a result, right out of the box you can create very nice dashboards about your logs, and mix them with your dashboards about metrics.

For many companies today business success depends on quality efficient build, test and delivery processes. Development and deployment environments become complex very quickly, even for small and medium companies. A contributing factor to this complexity is companies’ adoption of microservices. This is where modern CI/CD solutions like CircleCI come in to help streamline these processes.

Yesterday, my colleague Mike Elsmore wrote a blog about sending metrics to Logz.io Infrastructure monitoring – now let’s analyze them by building Grafana visualizations! Once you’ve started to send metric data to Logz.io, how do you visualize and interpret that data so that it’s useful for you? In Logz.io Infrastructure Monitoring, we use Grafana to provide dashboards and bring meaningful information to light.

We've added the ability to see detailed webhook logs to Oh Dear, showing both the sent request and the received response for all notifications we have sent.

Most enterprises already have a reliable logging and monitoring system in place, so why should you worry about it in the context of Kubernetes? Well, traditional logging and monitoring tools are designed for stable infrastructure and application deployments. Cloud native environments, on the other hand, are highly dynamic. The IT world has changed and so must your toolkit.