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Joins, pipes and more with the new Elasticsearch Query Language

The new Elasticsearch Query Language is a flexible, powerful, and robust query expression language to interrogate data. In this session learn how ESQL provides a superior query UX, a piped query language with join capabilities that fundamentally transforms and expands the analytics and data processing of Elasticsearch.

Elasticsearch and OpenSearch - not the same thing

Do you understand the differences between Elasticsearch and OpenSearch? We’ll lay them out for you. You’ll find that our take on emerging technologies is fundamentally transforming the opportunity to solve problems through search. Learn about innovation in areas like vector search and hybrid scoring or support for third-party natural language processing that help you unlock possibilities for new classes of searches through the application of machine learning. The result? Increased relevance with less burden on the developer and administrator. In this session, you'll learn all about these innovations, and how you can take advantage of them to drive success.

Using search effectively in taxonomies and correctly modeling your domain in Elasticsearch

Finding matches when using a taxonomy is a common problem. A notable challenge is mapping a user’s query to the entity (or results) expected when searching for an entity inside a catalog mapping. Functional textual search models tend to rely on exact match or partial match, but both can lead to a frustrating experience when users aren’t familiar with the domain. Basic models often fail to support user typos, synonyms, acronyms, and/or hyponyms/hypernyms. Learn how to tackle these challenges and make search more intuitive when using a taxonomy.

Elastic Enterprise Search 8.7: New connectors, extraction rules for web crawler, and search analytics client beta

Elastic Enterprise Search 8.7 is packed with features designed to improve content ingestion and search experiences. With this release, the MySQL connector adds advanced filtering capabilities, allowing you to filter and ingest large volumes of data from MySQL databases more efficiently.

Surface and Confirm Buggy Patterns in Your Logs Without Slow Search

Debugging with logs in distributed systems can be a pain. It’s tough to search raw data looking for a pattern, relating potential causes with other logs, and checking trace and metrics data for more confirmation. Is finding one pattern enough? What if there are other problems? Who knows how many colliding factors are relevant? At Honeycomb, we’re flipping the script on the log search problem. Hear our resident experts, (former Splunk Ninja) Michael Wilde and Andy Dufour, discuss how Honeycomb customers have technically evolved their log analysis process to achieve fast pattern detection, skipping the search grep/search loop entirely.

How Geometric Search Works for Hexagons in Elasticsearch

Geographic grid systems allow zooming into maps at progressively higher resolutions and finer grids. For rectangular grids, this is very simple, but for hexagonal grids, the situation is much more complex, since child hexagons are not fully contained within parent hexagons. This video demonstrates how we can still achieve efficient parent-child search in Elasticsearch using the H3 hexagonal grid.

The Unreasonable Effectiveness of Search Operators: Introducing 'send' Operator

Cribl Search is a powerful tool that allows users to search and analyze data at rest, quickly and efficiently. But what if you need to send your search results to a different system for further analysis, audit, or compliance purposes? For instance, consider the following use cases: That’s where send operator comes in.

OpenSearch vs Elasticsearch: Which One Is Better to Use?

Whenever we start a search consulting project from scratch, the obvious question is: which search engine to use? We’ve talked about Elasticsearch vs Solr before, but here we’ll compare Elasticsearch with its fork, OpenSearch. Chances are, if you need to decide between the two, you’ll be looking at a few dimensions.

Cribl Search 4.1: More Data, More Automation, and a More Intuitive User Interface

It’s been less than 4 months since we released Cribl Search, the first federated query engine focused on observability and security data. The reception has been tremendous. Customers, partners, prospects, and even our internal teams were overjoyed by the initial offering but have been anxiously awaiting the promises of the next release. The wait is over!

Python Elasticsearch Tutorial - How to use Python Elasticsearch client

Elasticsearch is a popular search engine that can be used to swiftly and almost instantly store, explore, and analyze huge volumes of data. It offers a distributed, multitenant full-text search engine with an HTTP web interface and schema-free JSON documents on top of Apache Lucene. In this tutorial, we will demonstrate how to communicate with an Elasticsearch cluster using a Python Elasticsearch client.