Databricks introduced Lakehouse//RT—powered by the new Reyden engine—to deliver millisecond analytics directly on Delta Lake, removing the need for separate serving databases. The summit also revealed LTAP, which merges OLTP and OLAP on a single data copy, and Lakebase, a PostgreSQL-compatible engine, signaling a push to unify transactional, analytical, and real-time workloads.

Databricks is buying Panther to bolster its security lakehouse platform as an alternative to legacy SIEM systems. Panther’s AI-driven SOC tools and over 100 pre-built integrations will automate threat detection, alert investigation, and response at scale.

This issue covers Databricks’ planned acquisition of Panther for AI-powered SOC threat detection and AWS’s new Continuum service that automates vulnerability discovery and fixes at machine speed. It also highlights Vercel’s secure data access tokens, Cisco and NVIDIA’s AI networking push, Iru’s config-as-code device management, Snowflake’s support for the Agentic Resource Discovery spec, and best practices against supply-chain attacks.

Databricks is launching a Software-Defined Storage ecosystem that uses the open-source OpenSharing protocol to link on-premises, edge, and private-cloud systems directly into its Data Intelligence Platform. This zero-copy approach lets teams run serverless compute and train models on local datasets under Unity Catalog governance without migrating any data.

This article breaks down how Databricks’ ai_parse_document and ai_query functions simplify PDF extraction in a proof-of-concept but introduce hidden challenges—ongoing costs, duplicate processing, non-deterministic outputs, and input noise—when you scale to a reliable production pipeline. It walks through the core issues and why you need additional system design for checkpointing, deduplication, deterministic validation, and PII handling before using it on real healthcare data.