Five years ago, Databricks coined the term 'data lakehouse' to describe a new type of data architecture that combines a data lake with a data warehouse. That term and data architecture are now commonplace across the data industry for analytics workloads.Now, Databricks is once again looking to create a new category with its Lakebase service, now generally available today. While the data lakehouse construct deals with OLAP (online analytical processing) databases, Lakebase is all about OLTP (online transaction processing) and operational databases. The Lakebase service has been in development since June 2025 and is based on technology Databricks gained via its acquisition of PostgreSQL database provider Neon. It was further enhanced in October of 2025 with the acquisition of Moonc [...]
Many enterprises running PostgreSQL databases for their applications face the same expensive reality. When they need to analyze that operational data or feed it to AI models, they build ETL (Extract, [...]
There is a lot of enterprise data trapped in PDF documents. To be sure, gen AI tools have been able to ingest and analyze PDFs, but accuracy, time and cost have been less than ideal. New technology fr [...]
Enterprise data teams moving agentic AI into production are hitting a consistent failure point at the data tier. Agents built across a vector store, a relational database, a graph store and a lakehous [...]
There is no shortage of AI benchmarks in the market today, with popular options like Humanity's Last Exam (HLE), ARC-AGI-2 and GDPval, among numerous others.AI agents excel at solving abstract ma [...]
A core element of any data retrieval operation is the use of a component known as a retriever. Its job is to retrieve the relevant content for a given query. In the AI era, retrievers have been used a [...]
The intelligence of AI models isn't what's blocking enterprise deployments. It's the inability to define and measure quality in the first place.That's where AI judges are now playi [...]
Data teams building AI agents keep running into the same failure mode. Questions that require joining structured data with unstructured content, sales figures alongside customer reviews or citation co [...]
Most enterprise RAG pipelines are optimized for one search behavior. They fail silently on the others. A model trained to synthesize cross-document reports handles constraint-driven entity search poor [...]