Elastic announced Search AI Lake, a first-of-its-kind, cloud-native architecture optimized for real-time, low-latency applications including search, retrieval augmented generation (RAG), observability and security.
The Search AI Lake also powers the new Elastic Cloud Serverless offering, which removes operational overhead to automatically scale and manage workloads.
With the expansive storage capacity of a data lake and the powerful search and AI relevance capabilities of Elasticsearch, Search AI Lake delivers low-latency query performance without sacrificing scalability, relevance, or affordability.
Search AI Lake benefits include:
- Boundless scale, decoupled compute and storage: Fully decoupling storage and compute enables effortless scalability and reliability using object storage, dynamic caching supports high throughput, frequent updates, and interactive querying of large data volumes. This eliminates the need for replicating indexing operations across multiple servers, cutting indexing costs and reducing data duplication.
- Real-time, low latency: Multiple enhancements maintain excellent query performance even when the data is safely persisted on object stores. This includes the introduction of smart caching and segment-level query parallelization to reduce latency by enabling faster data retrieval and allowing more requests to be processed quickly.
- Independently scale indexing and querying: By separating indexing and search at a low level, the platform can independently and automatically scale to meet the needs of a wide range of workloads.
- GAI optimized native inference and vector search: Users can leverage a native suite of powerful AI relevance, retrieval, and reranking capabilities, including a native vector database fully integrated into Lucene, open inference APIs, semantic search, and first- and third-party transformer models, which work seamlessly with the array of search functionalities.
- Powerful query and analytics: Elasticsearch’s powerful query language, ES|QL, is built in to transform, enrich, and simplify investigations with fast concurrent processing irrespective of data source and structure. Full support for precise and efficient full-text search and time series analytics to identify patterns in geospatial analysis are also included.
- Native machine learning: Users can build, deploy, and optimize machine learning directly on all data for superior predictions. For security analysts, prebuilt threat detection rules can easily run across historical information, even years back. Similarly, unsupervised models perform near-real-time anomaly detections retrospectively on data spanning much longer time periods than other SIEM platforms.
- Truly distributed - cross-region, cloud, or hybrid: Query data in the region or data center where it was generated from one interface. Cross-cluster search (CCS) avoids the requirement to centralize or synchronize. It means within seconds of being ingested, any data format is normalized, indexed, and optimized to allow for extremely fast querying and analytics. All while reducing data transfer and storage costs.
Search AI Lake powers a new Elastic Cloud Serverless offering that harnesses the innovative architecture’s speed and scale to remove operational overhead so users can quickly and seamlessly start and scale workloads. All operations, from monitoring and backup to configuration and sizing, are managed by Elastic – users just bring their data and choose Elasticsearch, Elastic Observability, or Elastic Security on Serverless.
“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,” said Ken Exner, chief product officer at Elastic. “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.”
Search AI Lake and Elastic Cloud Serverless are currently available in tech preview.
The Latest
In Part 1 of this two-part series, I defined multi-CDN and explored how and why this approach is used by streaming services, e-commerce platforms, gaming companies and global enterprises for fast and reliable content delivery ... Now, in Part 2 of the series, I'll explore one of the biggest challenges of multi-CDN: observability.
CDNs consist of geographically distributed data centers with servers that cache and serve content close to end users to reduce latency and improve load times. Each data center is strategically placed so that digital signals can rapidly travel from one "point of presence" to the next, getting the digital signal to the viewer as fast as possible ... Multi-CDN refers to the strategy of utilizing multiple CDNs to deliver digital content across the internet ...
We surveyed IT professionals on their attitudes and practices regarding using Generative AI with databases. We asked how they are layering the technology in with their systems, where it's working the best for them, and what their concerns are ...
40% of generative AI (GenAI) solutions will be multimodal (text, image, audio and video) by 2027, up from 1% in 2023, according to Gartner ...
Today's digital business landscape evolves rapidly ... Among the areas primed for innovation, the long-standing ticket-based IT support model stands out as particularly outdated. Emerging as a game-changer, the concept of the "ticketless enterprise" promises to shift IT management from a reactive stance to a proactive approach ...
In MEAN TIME TO INSIGHT Episode 10, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Generative AI ...
By 2026, 30% of enterprises will automate more than half of their network activities, an increase from under 10% in mid-2023, according to Gartner ...
A recent report by Enterprise Management Associates (EMA) reveals that nearly 95% of organizations use a combination of do-it-yourself (DIY) and vendor solutions for network automation, yet only 28% believe they have successfully implemented their automation strategy. Why is this mixed approach so popular if many engineers feel that their overall program is not successful? ...
As AI improves and strengthens various product innovations and technology functions, it's also influencing and infiltrating the observability space ... Observability helps translate technical stability into customer satisfaction and business success and AI amplifies this by driving continuous improvement at scale ...
Technical debt is a pressing issue for many organizations, stifling innovation and leading to costly inefficiencies ... Despite these challenges, 90% of IT leaders are planning to boost their spending on emerging technologies like AI in 2025 ... As budget season approaches, it's important for IT leaders to address technical debt to ensure that their 2025 budgets are allocated effectively and support successful technology adoption ...