Lakebase

Electric joins Databricks to bring WASM Postgres to AI agent sandboxes

Electric is joining Databricks to bring WASM Postgres and agent-focused data primitives to AI agent sandboxes. This extends Databricks' Postgres capabilities to the edge, allowing lightweight open-source

Backstage with Lakebase, part 3

In the first part of this series, running Backstage on Databricks Lakebase gave us...

Simplify AI agent orchestration with Lakebase Postgres

Learn how to turn two Lakebase Postgres tables into a durable, crash-resilient queue for long-running AI agent tasks without operating an external broker, cache, or scheduler. This fully Databr

Branching databases like code: a CI/CD pattern for Lakebase, in production at Glaspoort

Glaspoort has implemented a production-ready CI/CD pattern for Lakebase that branches development and acceptance environments directly from production to avoid the "reset-from-parent trap." This architecture enables a per-PR workflow where disposable branches are tested against live app images, treating migrations as the source of truth and utilizing a velocity-first promotion model backed by stack revalidation safeguards.

Building a soccer coaching app on Databricks

Coach's Corner is a Databricks App that processes 25 fps match tracking data into a sub-second 2D/3D tactical bench with replays, event analytics, a scout chat, and an opponent-dossier agent. The end-to-end solution is powered entirely on the Databricks platform, utilizing Lakeflow pipelines for data refinement, DBSQL and Lakebase for rapid querying, and Unity Catalog-governed AI tools like Genie, Vector Search, and MLflow tracing.

What happens in the milliseconds after you tap pay

This sample Databricks App demonstrates how to achieve low-latency real-time fraud scoring by pairing route-optimized Model Serving with Lakebase Postgres for online feature lookups. Under load testing of 5,000 requests, this architecture achieved end-to-end latencies of 27 ms at p50 and 37 ms at p95 while maintaining a 100% success rate.

Data-Native AI Agents: Why Agents Must Move to Your Data

AI agents must run directly within your data stack rather than in a separate, external stack to avoid compounding penalties like fragmented governance, high egress costs, and latency. By deploying data-native agents on the Databricks Data Intelligence Platform, enterprises can leverage an integrated stack of Unity Catalog, AI Search, MLflow, Lakebase, and AI Gateway to ship secure, trusted AI features faster.

Foundational context: Cross-industry & function-specific accelerators for Lakebase

Lakebase has launched a suite of partner-led, cross-industry, and function-specific accelerators designed to automate database migrations, power stateful memory for agentic AI, and deliver ready-to-deploy business applications. Databricks practitioners can leverage these solutions to safely rehearse legacy system cut-overs using database branching, maintain real-time context for autonomous agents, and quickly drive business value across finance, marketing, sales, and supply chain operations.

Profile-based authentication now takes precedence over environment variables, and deployment bugs affecting PyDABs-generated permissions and resource grants with --select have been fixed. New experimental job_runs resources are available, along with UC volume path support and options to hard-delete Lakebase branches or assume control of existing postgres databases on bundle deploy.

From monolith to Lakebase to LTAP: rethinking the database from storage up

Lakebase makes Postgres compute stateless by externalizing the log and data files into independent cloud services, unlocking unlimited storage, elastic compute, durable writes, and instant branching. LTAP further stores operational data once in open columnar formats that both Postgres and Lakehouse engines read, enabling analytics on fresh data without CDC pipelines or a second copy.

What Is Serverless PostgreSQL?

Serverless PostgreSQL decouples compute and storage, scaling independently and charging only for active usage. It's ideal for bursty workloads, but less suited for always-on, latency-sensitive applications, with Lakebase architecture unifying transactional and analytical workloads.

What if the answer was already in your data?

Kythera Labs' AI agents, built on Databricks, now provide health system leaders with governed, trustworthy answers to strategic questions from 339 billion claims. A Louisiana health system saw 150% more visibility into patient encounters and $3.8M in estimated annualized value in 10 days.

The CLI improves SSH error reporting with server logs, extends timeouts to 45 minutes for GPU accelerators, fixes environment variable handling to prevent default profile fallback, and corrects Cursor integration hanging. Bundle deployments prevent WAL corruption after failed deploys, fix resource references used multiple times within the same field, add postgres_roles and postgres_databases resources for Lakebase branches, and eliminate spurious recreates when names are backend-normalized.

What’s coming next to Free Edition

Databricks Free Edition now includes every core practitioner feature, expanding with Genie Code, GPUs, Lakebase, Lakeflow Designer, and Agent Bricks. This gives users a complete, free toolkit for building end-to-end data and AI projects.

What’s new in Databricks Platform security and compliance at Data + AI Summit 2026

Automatic Identity Management (AIM) for Entra ID is now GA on AWS and GCP, with AIM for Okta in Public Preview, alongside new Context-Based Ingress policies and expanded Private Link support for Lakebase and account-level services. Databricks also announced new certifications, regional compliance programs, broader AWS GovCloud support for AI services, and upcoming FedRAMP High support on Azure Commercial.

Sciene AI Companion: building an autonomous Customer Success platform on Databricks

Sciene built an autonomous Customer Success platform on Databricks, enabling AI-powered CSMs to standardize and scale work with context-aware emails, meeting decks, and account diagnostics. This end-to-end Databricks solution, leveraging Delta Sharing, Lakebase, and SQL Warehouses, significantly improved productivity and saved up to 6x time on key workflows.

Announcing Lakebase Search: agent-native retrieval built into Lakebase Postgres

Today, we're introducing Lakebase Search: hybrid vector and full-text retrieval built...

Unifying Data and Governance in the Agentic Era: What’s New with Azure Databricks

Azure Databricks now offers new capabilities for unifying data and governance in the agentic era, including the industry's first true LTAP architecture, serverless Postgres database branching, and millisecond-level response times via Lakehouse//RT. These updates also bring Genie for Microsoft Teams and M365 Copilot, the new Azure Databricks Excel Add-in, and Azure Databricks CustomerLake, a lakehouse-embedded Agentic CDP.

Enabling Evolutionary Database Development: Database branching with Lakebase, the conclusion

Lakebase now supports database branching, enabling evolutionary database development. This concludes the series on Lakebase's operationalization of evolutionary database design.

Forward Deployed Engineering: Delivering Business Outcomes with AI

Databricks is launching its Forward Deployed Engineering (FDE) organization to accelerate customer business outcomes with AI, pairing the Lakehouse platform with embedded, engineering-led delivery. This new approach moves beyond migration and pipeline building to solve business problems with production AI agents, as demonstrated by customers like Fox, JPMC, and Qualcomm.

How ERGO Hestia reduced time-to-market with Lakebase and Mosaic AI Model Serving

ERGO Hestia modernized its real-time pricing engine with Databricks Lakebase and Mosaic AI Model Serving, reducing time-to-market by unifying data, features, and decisions for millisecond pricing. This eliminated extraction overhead and fragmented governance from their previous multi-hop architecture, enabling faster model deployment and instant market response.

How Ecolab rebuilt retail intelligence on Databricks and Anthropic Claude

Ecolab rebuilt retail intelligence on Databricks and Anthropic Claude, converting 700-page FDA manuals into real-time answers for frontline staff using Foundation Model APIs and cutting compliance report compilation from two weeks to under two minutes. The solution, a native Databricks App with Lakebase Postgres and Unity Catalog, unifies nine siloed data sources and employs a multi-agent orchestration framework with Judge LLMs and MLflow tracing for personalized, continuously refined intelligence.

Enabling Evolutionary Database Development: database branching with Lakebase, continued

This series revisits the methodolgy of Evolutionary Database Design, twenty years...

Databricks Asset Bundles now features a select flag for plan and deploy commands to target specific resources alongside their transitive dependencies. The experimental open command now supports all bundle resource types with workspace URLs

Bring Databricks into Kiro IDE with the AI Dev Kit Power

The Databricks AI Dev Kit Power now offers a one-click setup to integrate Kiro IDE with the full Databricks platform, providing AI-assisted development grounded in your workspace's Unity Catalog metadata. This new path, alongside a lighter PAT-based option, ensures your AI assistant writes SQL with actual columns and respects all row, column, and tag-based grants.

Enabling Evolutionary Database Development: database branching with Lakebase

Why this series existsThe methodology described in Evolutionary Database Design and...

Introducing Always-On pricing: automatic savings for Databricks Lakebase

Databricks Lakebase now offers Always-On pricing, providing serverless flexibility with a 25% lower price on baseline capacity for established production workloads. Activate with a single toggle to disable scale-to-zero and set an autoscaling range, then after 24 hours of continuous use, baseline capacity bills at the Always-On rate while spikes bill at standard Autoscaling rates.

How the lakebase architecture stays resilient to cloud failures

Lakebase's architecture is built for resilience to cloud failures, not patched for it, by using stateless Postgres compute on zone-redundant storage and separating hot-path control-plane operations. This approach, validated through chaos testing and per-database availability tracking, addresses the unique reliability demands of agent workloads that start tens of millions of databases daily.

Announcing Lakebase Change Data Feed (CDF)

Lakebase Change Data Feed (CDF) is now in Public Preview, eliminating pipeline sprawl from operational databases by exposing every table's changes through Unity Catalog Managed Tables. This enables native CDC governed end-to-end without sidecar infrastructure, allowing operational data to function as the native Bronze layer in the medallion architecture.

Building a FHIR-native health data platform on Databricks Lakebase

Health Samurai's Aidbox now runs natively on Databricks Lakebase, providing a FHIR-native health data platform that standardizes clinical data at ingestion and makes it instantly available for Spark, ML, and AI. This architecture inherently delivers compliance with CMS-0057 and ONC mandates, eliminating the need for separate compliance workstreams.

The Databricks

Unlock seamless and cost-effective marketing campaigns with Lakebase

Lakebase Postgres, a serverless OLTP database, now scales to zero between marketing campaign spikes, eliminating underutilized database costs for personalization workloads. Native Synced Tables remove Lakehouse-to-OLTP pipeline burdens, letting marketing teams ship new customer segments to platforms like SAP Engagement Cloud in just a few clicks.

How to Build Real-Time Fraud Detection using Spark Real-Time Mode and Lakebase

Build real-time fraud detection with sub-second intervention using Spark Real-Time Mode and Lakebase. This unified platform processes high-throughput data streams, executes low-latency ML models, and serves explainable fraud scores to reduce detection lag and operational complexity.