Lakebase
Recent items mentioning Lakebase across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
Databricks has launched Lakebase as a fully-managed serverless Postgres database that runs on a data lake, decoupling compute and storage for high scalability and low latency 9. This architecture makes Postgres compute stateless by externalizing logs and data files into independent cloud services, enabling features like unlimited storage, elastic compute, and instant branching 5. Lakebase is a key component of the new LTAP offering, which unifies lakebased and lakehouse data to eliminate ETL and enable analytics on fresh operational data without CDC pipelines 15.
Generated daily from the 10 most recent items mentioning Lakebase. Click any [N] to jump to the source.
ReleasesDatabricks launches across the Data + AI stack in 90 seconds
Databricks announced LTAP to unify lakebased and lakehouse data, eliminating ETL and enabling a single copy of data for analytical and operational needs. They also introduced Unity AI Gateway for governance, Genie Ontology for enterprise knowledge graphs, and open-sourced Omniant for managing multiple coding agents.
Lakebase CDF databricks error synced
From monolith to Lakebase to LTAP: rethinking the database from storage up
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.
Fortifying Enterprise Healthcare Databricks Lakebase with the Security Triad
NewsWhat’s coming next to Free Edition
Databricks announces the availability of Genie, GPUs, Agent Hooks, Lakehouse, and Lake Flow Designer on its Free Edition. This update provides virtually all of Databricks' production platform features for free, enabling users to learn and build data and AI projects.
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.
EventsInside Lakebase: fully-managed serverless Postgres – Nikita Shamgunov, VP, Engineering, Databricks
Lakebase is a fully-managed serverless Postgres database that runs on a data lake, offering familiar, nimble, and mission-critical features. It achieves high scalability, low latency, and cross-cloud disaster recovery by decoupling compute and storage, re-architecting storage with safekeepers and page servers, and integrating with the lake.
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.
Features in Motion: Three Patterns for Real-Time ML in Databricks Lakebase
ReleasesIntroducing LTAP (Lake Transactional/Analytical Processing): a new data processing architecture
Databricks introduces LTAP (Lake Transactional/Analytical Processing), a new architecture that unifies transactional and analytical workloads by automatically converting row-oriented OLTP data into column-oriented formats (Delta/Iceberg) directly in the data lake. This eliminates the need for fragile CDC pipelines, providing real-time analytics on fresh data without impacting OLTP performance.
EventsHow Mastercard standardizes on Lakebase to power agentic operations
Mastercard uses Lakebase to standardize its agentic operations, creating a shared foundation for services like the "virtual C-suite" for small businesses and secure multi-tenant solutions for thousands of issuing banks. This standardization enables rapid development of AI agents with embedded governance and trust, allowing them to learn from each other and scale effectively.
Zero Code REST Integration for Modern HealthCare Vitals via Databricks Lakebase Data API
How to prevent users from creating Lakebase compute?
EventsRecap of product announcements from Data + AI Summit 2026 | Day 1
Databricks announced several new products and features at the Data + AI Summit 2026, Day 1, including the Genetic Data Foundation, Lakehouse RT, Lake Base with disaster recovery, Lake Flow, Genie Ontology, Unity AI Gateway, Omnigent, and various Genie agents (Genie 1, Genie Code, Genie Agents). They also introduced new applications like Lake Watch for SIM and Customer Lake for CP.
SSH connection error messages are improved with server logs, and the GPU accelerator startup timeout is increased. Bundle deployments now correctly handle Unicode in variable references and prevent drift from backend schema normalizations.
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.
Databricks Launches LTAP: A Unified OLAP/OLTP Data Architecture
--- top comments --- [epistasis] > The New Data Foundation for the Agentic Era Look, this announcement seemed exciting, but I'm significantly less excited when I come across a completely unrelated tie-in to AI. It breaks the illusion, and I'm reminded that it's just another PR announcement, and this is probably not going to impact my life at all in any way ever. So I'm off to the next article instead of reading any more. [mohsinimam] Curious how is the final format of the data in LTAP storage - is it columnar? If so then what happens to OLTP performance - the blog and all info speaks to OLAP performance but what about your app [mathisd] > No performance tradeoffs, for any workload: Transactional workloads run in standard Postgres with full ACID semantics. Analytical workloads run across the full Lakehouse at any scale and concurrency. Each scales independently, and because there's no data movement between systems, operational and analytical results are always in sync — with no copies or shadow infrastructure. How can there be no performance trade-off if storage is handled by PostGres and there is no data movement to convert it to columnar ? This deserve a technical explanation because this seems impossible. [geophph] Lakebase + Lakehouse = Lake [drchaim] No benchmarks, no pricing, no examples..
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.
Lakebase CDF — Stream Postgres Changes into Your Lakehouse Without the Connector Overhead
EventsDatabricks News: CLI v 1.0.0, AI-tools, databricks Docker, DABs UI sync, mutators
The video demonstrates new Databricks features, including the GA release of CLI 1.0.0, UI sync for DABs, Python mutators for bundle extension, and new Docker image options for custom runtimes. It also covers serverless pipeline orchestration, enhanced autoscaling for Lakebase and apps, serverless interactive execution timeout, and auto-scoping for access tokens.
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.
HealthCare Prior Authorizations with Databricks Lakebase Vector Search
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.
Building a Real-Time Field Sales App on Databricks with Lakeflow, Lakebase, and Mosaic AI
NewsEasy Migration from Postgres to Databricks Lakebase
The video demonstrates a tool for migrating existing PostgreSQL databases to Databricks Lakebase, highlighting potential compatibility issues like session state, extensions, and authentication that require architectural adjustments. It shows how to validate a PostgreSQL database for Lakebase compatibility and then perform a migration using a CLI tool, emphasizing the speed and ease of the process for straightforward databases.
Enabling Evolutionary Database Development: database branching with Lakebase, continued
This series revisits the methodolgy of Evolutionary Database Design, twenty years...
The experimental open command now supports opening more Databricks resource types directly from the CLI. Databricks Bundles gain a --select flag for partial deployments, improved retry logic for transient errors, and support for Terraform references.
Apps and Lakebase scaling
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.
TutorialsSafe AI-Driven Development with Lakebase Branches
Databricks Lakebase branches enable instant, cost-efficient database branching using copy-on-write, allowing developers to test features in isolated environments without affecting production data. The video demonstrates creating and managing these branches via the Lakebase console and Databricks CLI, and shows how to integrate them into an agentic development workflow for safe AI-driven development.
Enabling Evolutionary Database Development: Database Branching with Lakebase
--- top comments --- [kevin-h] Jen is the developer character from Evolutionary Database Design. In that essay she implemented a database refactoring – splitting an inventory_code field into location_code, batch_number, and serial_number – as a routine user story, illustrating that DBAs and developers can collaborate, schemas can evolve in small increments, and migrations carry the change forward safely. The series picks up with Jen twenty years later. The methodology she follows is the same one she followed in 2003. What's new is the substrate underneath her workflow: copy-on-write database branching, which makes the practices she has been reading about operationally real at production scale. Across the three parts of this series she is the same Jen at three scopes – her day (Part 1), her new playbook (Part 2), and her team (Part 3). This is Part 1
Enabling Evolutionary Database Development: database branching with Lakebase
Why this series existsThe methodology described in Evolutionary Database Design and...
Lakebase engineering team talks database resiliency and cloud failures
Sync Tables: Unity Catalog to Lakebase - Materialized Views triggered mode
Lakebase Data API private access with Public Network Access disabled
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.
Context Engineer Associate Beta Ex︁am + free attempt at DAIS
Context engineering is quickly becoming one of the key skills for building reliable AI agent systems. Databricks has just introduced the **Databricks Context Engineer Associate** **Ex︁am**, focused on designing, assembling, and governing the information AI agents receive at inference time - including prompts, retrieval systems, memory, tools, governance, and evaluation. The ex︁am is currently available as a **live beta at Data + AI Summit 2026**, and Databricks states that **one free onsite exam attempt will be offered during Summit**. Walk-ins only, one per attendee. Great opportunity for anyone working with GenAI, AI agents, Vector Search, Unity Catalog, MLflow, MCP, or Lakebase. [https://www.databricks.com/learn/certification/context-engineer-associate](https://www.databricks.com/learn/certification/context-engineer-associate)
Calling All Admin Teams - How Much Would a Genie Code-Embedded Databricks Portal App Help Out with Broader IT Administration?
Hey r/databricks, Full disclosure, I have a few years of experience as an Account Administrator for a decently sized Databricks deplyoment, but wanted to get the subreddit's take on this idea. Advancements like Unity Catalog and Genie Code Agent Mode have made the Admin experience WAY better, but could an all-in-one App help out with some of the outstanding pain points that large IT organizations deal with? The biggest "***solvable"*** **problems that I dealt with were:** 1. **Dealing with similar tickets over and over again** 2. **Explaining our organizations best practices over and over again** 3. **Communicating the same coding/infra concepts over and over again** It was difficult to fault end-users since in many cases they were new and the concepts were not their specific area of technical competence, but it did always irk me how much less productive it made our team, and in turn, the entire organization. **However, now that Apps, Lakebase and Genie Code are all avaiable and take out a tremendous amount of overhead (security, governance, infra, etc.). I think it's never been easier to rapidly stand up useful solutions to fully address each and every one of these challenges!** My thoughts are that a "One-Stop Portal" that provides users access to a centralizes area for: * Best Practices (curated by Admins) * A Discussion Board (all moderated by Admins) * Other built-in helpful utilities could be exactly what many organizations are lacking right now. Additionally, now that Genie Code has Workspace Instructions, it can intelligently push the Portal App to users if prompted with organization-specific questions. Unlike other solutions, users would now be natively pushed to answers as opposed to have to the heavy lifting of finding everywhere to *maybe* get the answer. Anyways, what do you all think of this, and how are you all using Databricks to work towards these common IT challenges right now? [Example Home Page](https://preview.redd.it/l6e2g9r8zi3h1.png?width=3456&format=png&auto=webp&s=2574be6b84b0ab3087d518958f2f5f4ca001434a) [Example Best Practices Page](https://preview.redd.it/h0r8g0lazi3h1.png?width=3456&format=png&auto=webp&s=eb0f923d6c4751ed501b3c8bca8a6f6bc7363c2d) [Example Q&A Page - huge time saver for admin teams!!](https://preview.redd.it/ejb4ejbczi3h1.png?width=3456&format=png&auto=webp&s=d0ff56d33c81dc5805d5819d19abb2c4425aed81) [Q&A Drilldown](https://preview.redd.it/s8dj35sezi3h1.png?width=3456&format=png&auto=webp&s=d5c56913fc892fd03d39f4b9cb29540f0473f38a) [SAS Code Conversion Utility](https://preview.redd.it/nbxx5qbgzi3h1.png?width=3456&format=png&auto=webp&s=a40ee34250252ae61b29f23c12e50c68dc8d6a3b)
Reminder: Share your Lakebase story and receive a $50 gift card!
OAuth tokens for interactive logins are now stored in the OS-native secure store by default, requiring re-authentication after upgrading. A new `databricks aitools` command group is added for installing Databricks skills into coding agents.
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.
TutorialsAI Agents That Remember: Building Stateful Systems with Lakebase
AI agents require four types of memory (working, episodic, entity, procedural) to be truly intelligent and stateful, which traditional databases struggle to provide. Databricks Lakebase, built on Postgres, offers a unified OLTP and OLAP solution with features like serverless auto-scaling and Git-style branching to manage these complex memory needs for AI agents.
Backstage with Lakebase, part 2
Lakebase enables running production OLTP applications like Backstage on a serverless Postgres surface within Databricks, offering 1-second database branching and sub-4-second point-in-time recovery for schema migrations. Unity Catalog unifies governance for operational databases, providing single SQL query auditing, automatic row-level security propagation to branches, and zero-ETL cost attribution for FinOps.
Clinical operations intelligence belongs on the Lakehouse
The Site Feasibility Workbench, an open-source Databricks App, now enables clinical trial site selection entirely within the Databricks workspace, eliminating external API calls and synchronization pipelines. This solution addresses the architectural challenge of disconnected clinical operations data, improving enrollment target attainment with TA-segmented LightGBM models and auditable SHAP-driven explanations.
Announcing Native Lakehouse Sync
Native Lakehouse Sync (Public Preview) now automatically replicates Lakebase Postgres data into Unity Catalog managed tables, eliminating pipelines and external compute. This enables live ML features, operational data as the Bronze layer with full SCD Type 2 history, and built-in audit capture, all with zero Postgres performance impact and no added cost.
MCP Marketplace Brings Real-Time Intelligence to Agentic Applications
MCP Marketplace now provides real-time external intelligence for agentic applications, with partners like You.com and Moody's offering governed data. Lakebase and Genie enable end-to-end workflows, allowing agents to maintain context and surface decisions to business users for review.
NewsDatabricks in 3 minutes. The unified data and AI platform, explained.
Databricks unifies diverse data sources into a single data lake, providing a governed platform for analytics and AI. It offers capabilities like fine-grained access control, natural language querying with AI, and company-wide intelligent agents.