Elastic, the company behind Elasticsearch and the Elastic Stack, announced the general availability of version 7.4 of the Elastic Stack.

Debuting in version 7.4 are several new features that simplify cluster administration and operations, introduce new aggregation and machine learning capabilities, and deepen the stack security experience.

Snapshot lifecycle management — Snapshot / restore is the best way to backup (and restore) data in Elasticsearch and is a critical part of nearly all production deployments. In 7.4, Elastic is introducing snapshot lifecycle management, which allows an administrator to define policies that manage when and how often snapshots are taken. This ensures that appropriate, recent backups are ready if disaster strikes or the data needs to be restored for other reasons.

Together with the index lifecycle management feature introduced in 6.7, snapshot lifecycle management dramatically simplifies operating a production cluster. By building these as native features of the Elastic Stack, administrators reduce the dependency on external tools and cron jobs, gain reliability and visibility, and can focus on defining the data protection policies that matter to their business.

Version 7.3 introduced data transforms to let users pivot their Elasticsearch data into entity centric indices on the fly. This transformative feature (pun intended!) is a foundational block for a suite of new analytics and machine learning methods — such as outlier detection, regression, classification, and more.

Elastic added a new Analytics tab in the Machine Learning app in Kibana to create, manage, and view your outlier detection jobs.

In addition to these two new methods, Elastic is also introducing several (13 to be precise) new out-of-the-box machine learning jobs to Elastic SIEM to detect common security threats on network and host activity data collected by Auditbeat. Examples include detecting anomalous processes, anomalous network port activity, and more.

With 7.4 Elastic doubled down on auto-instrumentation in Elastic APM, expanding APM agents to support the plug-and-play experience for more common programming frameworks. Most notably, these additions include Angular support in the RUM agent and .NET framework support in the .NET agent, which are both very common in existing enterprise applications. In addition, Elastic built on the RUM geolocation data introduced in the previous release by adding a new "performance by geographic region" breakdown in the APM UI, allowing users to zero in on region-specific problems and troubleshoot them faster. Finally, structured filters in the Elastic APM UI help analysts search trace data even faster, resulting in shorter resolution cycles.

Every Elastic Stack release brings new ways to automatically monitor the health of your critical infrastructure and services. The 7.4 release adds support for ingesting metrics from several more AWS services, including Elastic Load Balancer (ELB), Elastic Block Storage (EBS), and CloudWatch Statistics, plus the ability to read server logs right from Amazon Simple Storage Service (S3). It also increased Kubernetes monitoring capabilities with additional metrics for scheduled jobs, and added a new StatsD module for collecting custom metrics using this common format. Database monitoring got a boost with many new metricsets and improved default dashboards for datastores like Oracle, Prometheus, and Postgres. Finally, Elastic added log analysis for IBM MQ, a critical messaging platform in many important enterprise applications.