Indices are an important part of Elasticsearch. Each index keeps your data sets separated and organized, giving you the flexibility to treat each set differently, as well as make it simple to manage data through its lifecycle. And Elastic makes it easy to take full advantage of indices by offering ingest methods and management tools to simplify the process.
As I’ve often talked about before, we have a “big tent” philosophy at Grafana Labs. We believe our users should determine their own observability strategy and choose their own tools; Grafana allows them to bring together and understand all their data, no matter where it lives. In practice, that means that we want to support data sources that our users are passionate about.
Cache is king for speedy data retrieval. So if you’re interested in how Elasticsearch leverages various caches to ensure you are retrieving data as fast as possible, buckle up for the next 15 minutes and read through this post. This blog will shed some light on various caching features of Elasticsearch that help you to retrieve data faster after initial data accesses.
Your logs, metrics, security, and trace data are all invaluable to you. They are mission critical for your observability and security needs. As your IT infrastructure grows and keeps generating more and more data, your data volumes and data storage needs go up accordingly. It can quickly become cost-prohibitive to indefinitely store all of it on your hottest machines.
For the 7.10 release of Elastic Workplace Search, we highlighted some of the new analytics logging capabilities and took you through a short walkthrough of some sample analysis scenarios. With the 7.11 release we introduced analytics fields, which open up new possibilities for exploring query and click data with helpful dashboards and visualizations.
We are excited to bring you a number of updates for using Elastic Cloud (Elasticsearch managed service) in the AWS Marketplace.
We're excited to announce that autoscaling is now available on Elastic Cloud. In our initial release, autoscaling monitors the storage utilization of your Elasticsearch data nodes and the available memory capacity for your machine learning jobs.
Back in our 7.10 release of the Elastic Stack, we announced the beta of our Ruby and Python clients for Elastic Enterprise Search. Now, with 7.11, both the Ruby and Python clients are generally available. We’ve also begun work on a PHP client. All client source code for both enterprise-search-ruby and enterprise-search-python is available on GitHub. Documentation on how to get started with each client is available on elastic.co.
The cold tier of searchable snapshots, previously beta in Elasticsearch 7.10, is now generally available in Elasticsearch 7.11. This new data tier reduces your cluster storage by up to 50% over the warm tier while maintaining the same level of reliability and redundancy as your hot and warm tiers.
