Highlights:

  • Elastic Search AI Lake can scale search across exponentially larger data volumes by decoupling storage from compute. This approach ensures rapid query performance for traditional structured data and unstructured information represented as vectors.
  • Elasticsearch caters to various use cases, including threat detection and application observability. It offers tools for visualizing networks and monitoring their performance.

Enterprise search technology provider Elastic N.V. aims to enhance accessibility to essential data for generative artificial intelligence, security, and observability workloads by launching its new Search AI Lake service.

The recent announcement introduces Elastic Search AI Lake, a fundamentally distinct offering from traditional Elasticsearch deployments. Elastic Search AI Lake can scale search across exponentially larger data volumes by decoupling storage from compute. This approach ensures rapid query performance for traditional structured data and unstructured information represented as vectors. This feature positions Search AI Lake as a precious data store for generative AI services, as believed by the company.

Elastic Search AI Lake is founded on the company’s widely adopted Elasticsearch technology, constructed on the open-source Apache Lucene project. Enterprises widely employ that platform to store, search, and analyze vast volumes of data in real-time.
It serves as the core of millions of applications worldwide, fulfilling the need for comprehensive search capabilities.

In addition to supporting general search functionality, Elasticsearch caters to various use cases, including threat detection and application observability. It offers tools for visualizing networks and monitoring their performance. More recently, Elastic introduced the Elasticsearch Relevance Engine, which integrates vector search with its traditional search engine. This allows it to handle unstructured audio, image, and video files.

However, these older offerings are limited by the coupling of storage with compute, creating scalability barriers. This issue is effectively resolved by decoupling these elements, explained Elastic Chief Product Officer Ken Exner.

He stated, “To meet the requirements of more AI and real-time workloads, it’s clear a new architecture is needed that can handle compute and storage at enterprise speed and scale – not one or the other. Search AI Lake pours cold water on traditional data lakes that have tried to fill this need but are simply incapable of handling real-time applications. This new architecture and the serverless projects it powers are precisely what’s needed for the search, observability, and security workloads of tomorrow.”

In an interview with a renowned tech media outlet, Elastic Chief Executive Ash Kulkarni stated that Search AI Lake is constructed on a fundamentally different architecture than traditional data lake offerings like Databricks Inc.’s Delta Lake and Snowflake Inc.’s cloud data warehouse. In contrast to those platforms, Elastic Search AI Lake integrates its search functionality directly into the data lake. This enables real-time exploration and querying of the data contained within, eliminating the requirement for predefined schemas.

Kulkarni noted that Search AI Lake provides dense vectors, hybrid search, faceted search, and relevance ranking features tailored to generative AI models and retrieval-augmented generation techniques.

Another distinction is that Elastic Search AI Lake does not utilize traditional data table formats like Apache Iceberg or Apache Hudi. This is because such architectures can impede data exploration, as highlighted by Kulkarni.

Users must include metadata and enable metadata searchability when data is inserted into a data lake table. Otherwise, it becomes exceedingly challenging to locate the data.
Search AI Lake, in contrast, adopts the Elastic Common Schema format and relies on the Elasticsearch Query Language. This approach allows data to be explored in a federated manner across Elastic clusters.

With Search AI Lake, Elastic seeks to establish itself as the data platform for generative AI models, which can experience significant enhancements through highly scalable vector search capabilities. By supporting these capabilities, large language models can enhance their knowledge by searching for the most recent and relevant data as it becomes available in real-time. This improves their responses and ensures they remain up-to-date with the latest information.

Search AI Lake is accessible as a standalone platform and the underlying technology for a new offering known as Elastic Cloud Serverless. Both options are available in technology now.