Category: Computing & data (Page 3 of 79)

DataStax announced the upcoming launch of DataStax HCDP (Hyper-Converged Data Platform), in addition to the upcoming release of DataStax Enterprise (DSE) 6.9. Both products enable customers to add generative AI and vector search capabilities to their self-managed, enterprise data workloads. DataStax HCDP is designed for modern data centers and Hyper-Converged Infrastructure (HCI) to support the breadth of data workloads and AI systems. It supports on-premises enterprise data systems built to AI-enable data and is designed for enterprise operators and architects.

The combination of OpenSearch’s Enterprise Search capabilities, with the high-performance vector search capabilities of the DataStax cloud-native, NoSQL Hyper-Converged Database, enables users to speed RAG and knowledge retrieval applications into production.

Hyper-converged streaming (HCS) built with Apache Pulsar is designed to provide data communications for a modern infrastructure. With native support of inline data processing and embedding, HCS brings vector data to the edge, allowing for faster response times and enabling event data for better contextual generative AI experiences.

HCDP provides rapid provisioning and data APIs built around the DataStax one-stop GenAI stack for enterprise retrieval-augmented generation (RAG), and it’s all built on the open-source Apache Cassandra platform.

https://www.datastax.com/press-release/datastax-launches-new-hyper-converged-data-platform-giving-enterprises-the-complete-modern-data-center-suite-ceeded-for-ai-in-production

Elastic, a Search AI company, today announced Search AI Lake, a 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. 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. Benefits include:

• Fully decoupling storage and compute enables scalability and reliability using object storage, dynamic caching supports high throughput, frequent updates, and interactive querying of large data volumes.
• Multiple enhancements maintain query performance even when the data is safely persisted on object stores.
• By separating indexing and search at a low level, the platform can automatically scale to meet the needs of a wide range of workloads.
• Users can leverage a native suite of AI relevance, retrieval, and reranking capabilities, including a native vector database integrated into Lucene, open inference APIs, semantic search, and first- and third-party transformer models, which work with the array of search functionalities.
• Elasticsearch’s query language, ES|QL, is built in to transform, enrich, and simplify investigations with fast concurrent processing irrespective of data source and structure.

https://ir.elastic.co/news/news-details/2024/Elastic-Announces-First-of-its-kind-Search-AI-Lake-to-Scale-Low-Latency-Search/default.aspx