Simplify configuring cross-cluster search, execute vector search faster, detect data drifts and log rate dips, stream Elastic Agent to Kafka, and authenticate Webhook connector using third-party security certificates with Elastic Stack 8.10.

Elastic Search 8.9 brings improvements to vector search and ingestion and presents hybrid search with RRF to combine vector, keyword, and semantic techniques. Public-facing search endpoints for indices are now available with search applications beta.

Elastic Stack 8.9 delivers significant speed-up of aggregations for metrics, faster and more reliable cross-cluster searches, and management of alert rules using Terraform.

Elastic Learned Sparse Encoder is an AI model for high relevance semantic search across domains. As a sparse vector model, it expands the query with terms that don't exist in the query itself, delivering superior relevance without domain adaptation.

In 7.11, we’re excited to announce schema on read in the Elastic Stack. We now offer the best of both worlds on a single platform. Schema on read for flexibility and schema on write for performance. We call our implementation runtime fields.

Introducing runtime fields, Elastic's implementation of schema on read. Now you can use either schema on write for performance or schema on read for flexibility. In this blog, we discuss how to get started with runtime fields.

Elasticsearch 7.2.0 is here! Learn about our new proximity ranking (geo and time), enhancements to SQL, and support for OpenID Connect.

Coming soon to Elasticsearch, you'll be able to implement the Distance Feature Query to use proximity (geographical and time) as part of your ranking score.