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, Transform, Load) data pipelines to move it into analytical systems. Those pipelines require dedicated data engineering teams, break frequently and create delays measured in hours or days between when data is written to a database and when it becomes available for analytics.For companies with large numbers of PostgreSQL instances, this infrastructure tax is massive. More critically, it wasn't designed for a world where AI agents generate and deploy applications at machine speed, creating new tables, events and workflows faster than any data engineering team can keep up.Databricks is making [...]
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 n [...]
Traditional ETL tools like dbt or Fivetran prepare data for reporting: structured analytics and dashboards with stable schemas. AI applications need something different: preparing messy, evolving oper [...]
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 [...]
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 [...]
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 [...]
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 [...]
AI agents – task-specific models designed to operate autonomously or semi-autonomously given instructions — are being widely implemented across enterprises (up to 79% of all surveyed for a PwC rep [...]
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 [...]