Hiya! Our Elasticsearch team is continually improving our index Lifecycle Management (ILM) feature. When I first joined Elastic Support, I quickly got up to speed via our Automate rollover with ILM tutorial. I noticed after helping multiple users set up ILM that escalations mainly emerge from a handful of configuration issues. In the following sections, I’d like to cover frequent tickets, diagnostic flow, and common error recoveries. All commands shown can be run via Kibana’s Dev Tools.

Rapid digital transformation partnered with increased cloud adoption have resulted in organizations generating unprecedentedly large volumes of data. This data is stored in disparate data repositories due to organizational boundaries, data protection, and privacy laws (e.g. GDPR). Additionally, it is stored across environment types with some kept in the cloud and often historical data and other sensitive data types are kept in on-premise environments contributing to more data silos.

Is enterprise data a benefit or a burden? Think about all of the data your organization generates and consumes in the digital age — from security event logs to application error messages, energy consumption to vendor contracts. There is so much, and all of it is usually stored in silos, making the data difficult to synthesize to provide better services, identify signals proactively, or make stronger business decisions.

We’re thrilled to announce that Elastic has been named a Leader in The Forrester Wave™: Cognitive Search, Q3 2021*, highlighting, in our opinion, our commitment to providing a set of tools that makes it quicker and easier to build great search experiences with Elasticsearch. In addition to receiving the highest score possible in the strategy category, Elastic also received the highest scores possible in the operations and market awareness criteria.

I’m thrilled to say that OpenSearch has reached general availability (GA) with the release of version 1.0. This release represents a significant milestone and noteworthy accomplishment for a new open source initiative that was only launched a few months ago. I vividly remember that moment at the beginning of the year when we all woke up to Elastic’s announcement that it would take Elasticsearch and Kibana off the Apache 2.0 OSS license.

Elasticsearch is a distributed search and analytics engine used for real-time data processing of several different data types. Elasticsearch has built-in processing for numerical, geospatial, and structured text values. Unstructured text values have some built-in analytics capabilities, but custom text fields generally require custom analysis. Built-in text analysis uses analyzers provided by Elasticsearch, but customization is also possible.

Even before COVID-19 forced nearly everyone to grapple with virtual work, most organizations had mountains of content scattered across numerous teams and dozens, if not hundreds, of apps. But now new research shows the cost of poor employee experiences for organizations: productivity, confidence, and opportunity.

In a previous blog, we covered how you can incorporate synonyms into your Elasticsearch-powered application. Here, I build upon that blog and show how you can combine stemmers and multi-word synonyms to take the quality of your search results to the next level.

TL;DR: We recommend that you use the new point-in-time functionality in Elasticsearch if you can. The scroll API is no longer recommended for deep pagination (even though it still works). Most data is constantly changing. When querying an index in Elasticsearch, you are essentially searching for data at a given point of time.