---
title: "Lakebase"
description: "45 posts about Lakebase, summarised, each linking to the original."
---

# Lakebase
> 45 posts about Lakebase, summarised, each linking to the original.

## Articles

### [Simplify AI agent orchestration with Lakebase Postgres](https://yomu.fyi/post/simplify-ai-agent-orchestration-with-lakebase-postgres.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Li Yu, Michelle JanneyCoyle, Jon Cormack, Yarri Bryn, Alec Sorensen, Darshana Nair
- Published: Jul 22, 2026

CLA and Databricks built a production document-processing application for auditing that reduces extraction time from hours to minutes without compromising quality, using Databricks-native services including Lakebase Postgres, Databricks Apps, Lakeflow Jobs, MLflow, and Unity Catalog Volumes. Lakebase serves as the orchestration layer’s single source of truth for tasks and execution attempts, coordinating long-running work, retries, leases, priorities, rate limits, costs, and status visibility. The queue uses Postgres patterns including FOR UPDATE SKIP LOCKED, priority and FIFO ordering, expiring leases for crash recovery, and database-backed concurrency controls. Databricks Jobs process PDFs through intelligent document processing and vision/LLM calls, while MLflow Tracing records execution and cost details and dashboard updates combine fast Postgres data with slower billing queries. In production, this architecture avoids external brokers and schedulers while providing durable task management, real-time visibility, and per-task cost attribution.


### [Branching databases like code: a CI/CD pattern for Lakebase, in production at Glaspoort](https://yomu.fyi/post/branching-databases-like-code-a-ci-cd-pattern-for-lakebase-in-producti.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Hadi Farhat, Gideon Spierings, Ricardo de Vries, Raymon Veldman
- Published: Jul 20, 2026

Glaspoort replaced a sprawl of outdated, one-off BI reports with a custom application that combines Databricks analytical tools with Lakebase, a serverless Postgres OLTP database. The application keeps curated lakehouse data in a read-only schema while application state occupies a separate schema, and supports development, acceptance, and production environments. Its CI/CD pattern treats database branches like code: long-lived environments branch directly from production, each pull request receives an ephemeral production-shaped branch, migrations are replayed, and a staging app image runs the full test suite against it. This topology makes periodic resets cheap, avoids deleting dependent branches, and preserves fresh test data; the team chose stacked promotion for velocity, retaining a separate crisis pipeline for urgent fixes.


### [Tech builds on AI. Finance protects the margin.](https://yomu.fyi/post/tech-builds-on-ai-finance-protects-the-margin.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Madelyn Mullen
- Published: Jul 17, 2026

AI-native tech companies must protect unit economics as agents accelerate changes in compute consumption, pricing, and revenue recognition, while gross margins remain below classic software levels. Finance teams built around extracts, spreadsheets, and monthly reconciliation can miss repricing changes, metering errors, and compute-commitment risk. The proposed foundation is an evolving ontology that keeps product, plan, usage, and cost meanings current, with Stripe data entering Unity Catalog through OpenSharing and Lakebase providing transactional Postgres on the lakehouse. Genie One uses that ontology to answer governed, sourced questions about gross margin, consumption revenue at risk, and compute spend, while people retain decision authority. The post describes organizations using Databricks to consolidate reporting, forecasting, workflows, and finance applications, positioning a shared data-and-AI platform as the path from an initial answer to an ongoing finance platform.


### [Building a soccer coaching app on Databricks](https://yomu.fyi/post/building-a-soccer-coaching-app-on-databricks.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Samwel Emmanuel, Sheridan Harris, Andrew Helmreich, Kush Patel, Nick Ragonese
- Published: Jul 17, 2026

Coach’s Corner, also called La Pizarra, turns high-frequency soccer tracking data into a bench-side application for replay, tactical analysis, scouting, standings, and agent-generated dossiers. Built as a Databricks App, it ingests NDJSON feeds at 25 frames per second through Auto Loader and Spark Declarative Pipelines, enforcing 46 data-quality expectations across bronze, silver, and gold layers. Liquid clustering supports 1–3-second DBSQL queries, while Lakebase synchronizes gold data to Postgres for millisecond replay reads and separates sequential playback from exploratory analytics. The scouting layer grounds Genie, Vector Search, a Unity Catalog-registered xG model, and an Agent Bricks supervisor in governed data, with Claude calls routed through the Unity AI Gateway, MLflow tracing, and a deterministic fallback. Together, these components are presented as a way to deliver traceable insights within seconds without forcing coaches to interpret raw tables or analysts to relay every result.


### [What happens in the milliseconds after you tap pay](https://yomu.fyi/post/what-happens-in-the-milliseconds-after-you-tap-pay.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Harsha Pasala, Subhadip Chanda
- Published: Jul 16, 2026

At checkout, fraud scoring must combine transaction inference with customer-specific rules while keeping latency low enough for an interactive payment. The retail-app sample pairs a FastAPI backend and React frontend in a Databricks App with Model Serving route optimization and Lakebase Postgres, where the model retrieves historical features and the backend reads profile controls. A transaction is scored first, then checked against daily spending, international-transaction, and country rules; pooled OAuth-authenticated connections and token-rotation handling avoid repeated handshakes and stale credentials. In the supplied benchmark, route-optimized calls reached 27.2 ms p50 and 37.3 ms p95 end-to-end, with feature lookup at 8.9 ms p50, CatBoost inference at 0.4 ms, and network overhead at 17.4 ms p50; actual performance varies by model.


### [Data-Native AI Agents: Why Agents Must Move to Your Data](https://yomu.fyi/post/data-native-ai-agents-why-agents-must-move-to-your-data.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Kaan Kuguoglu, John Karlsson
- Published: Jul 15, 2026

Enterprise AI pilots often move data into separate vector databases, SaaS LLMs, or serving layers, creating governance gaps, compounded latency, fragmented costs and observability, and duplicated lifecycle work. The post advocates data-native agents: models, agents, tools, retrieval, and memory run inside the governed data platform, with policy enforced during query planning and computation rather than after responses are produced. It argues that post-hoc controls cannot undo sensitive information encoded in aggregations and can trigger token-burning retry loops. For state and memory, it presents Lakebase, managed PostgreSQL within Databricks, as transactional storage and a shared source of truth for multi-agent swarms. The described platform pattern combines Unity Catalog, Unity AI Gateway, Model Serving, MLflow 3, AI Search, Lakebase, and business-context services, and recommends inventorying workloads already outside the perimeter before closing seams incrementally.


### [Foundational context: Cross-industry & function-specific accelerators for Lakebase](https://yomu.fyi/post/foundational-context-cross-industry-function-specific-accelerators-for.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Amit Singh
- Published: Jul 14, 2026

Databricks presents Lakebase as a fully managed, serverless, standard Postgres database for combining operational and analytical workloads on its Data + AI Platform. The platform separates compute from storage, integrates with the lakehouse through Synced Tables and Lakebase CDF, and uses Unity Catalog for governance; copy-on-write branching and autoscaling to zero are described as core infrastructure primitives. The post showcases partner-built, ready-to-deploy accelerators spanning technology, finance, marketing, sales, supply chain, human resources, customer service, and operations. Examples include PostgreSQL migration assessment, multi-agent Genie orchestration, stateful enterprise agents, autonomous data reliability, governed contact-center intelligence, and project operations management. These offerings package Lakebase patterns into migration controls, domain-specific solutions, and agent frameworks intended to accelerate modernization and reduce transformation complexity.


### [Introducing Feature Views](https://yomu.fyi/post/introducing-feature-views.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Nick Joung, Ian Ackerman, Julia Powell
- Published: Jul 10, 2026

Feature Views are presented as a framework for creating managed feature pipelines in Databricks, addressing duplicated logic, training/serving skew, backfills, online-store plumbing, and governance for real-time ML. A Feature View defines the source, entity, time-series column, and computation once; Databricks uses it to generate point-in-time-accurate historical data, then materializes pipelines for batch or streaming production use. Registered in Unity Catalog, materialized features receive lineage and access controls, while MLflow records model dependencies and Model Serving retrieves required features without custom lookup code. For Kafka-sourced streaming features, Spark Realtime Mode, Lakebase, and Model Serving support rolling-window updates with reported end-to-end p99 latency of 200ms from event to online availability. The announcement says Feature Views are in Public Preview and can be developed through the Feature Engineering Client SDK and Genie Code, with streaming materialization requiring an Enterprise-tier workspace in a region supporting Lakebase.


### [The ambulatory intelligence gap](https://yomu.fyi/post/the-ambulatory-intelligence-gap.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Morgan Wilkie, Bryan Smith, Holly Burke, Mary Ellen
- Published: Jul 10, 2026

Health systems face an ambulatory intelligence gap: access, provider capacity, referral retention, panel management, and financial performance are interconnected, while information remains scattered across disconnected systems. Health Catalyst’s Ambulatory Intelligence combines AI with nearly two decades of healthcare improvement expertise, deploying directly in a customer’s Databricks workspace so sensitive data stays within the health system’s environment. It uses a medallion-based semantic layer, Unity Catalog for governance, Lakebase for low-latency serving, and Genie alongside dashboards to help leaders investigate why metrics change. The solution ships with prebuilt metrics across Access Optimization, Revenue Intelligence, Panel Management, and Referral Insights, plus cross-domain scorecards and configurable terminology and workflows. Reported outcomes from supported improvement work include increased revenue and encounters at Thibodaux Regional, 55,000 closed care gaps at INTEGRIS Health, and higher outpatient visits with fewer cancellations without reschedules at WakeMed; future plans include models based on prior outcomes and agentic capabilities.


### [Beyond dashboards: Introducing Decision Execution Platforms](https://yomu.fyi/post/beyond-dashboards-introducing-decision-execution-platforms.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Marc Solomon, Marcello Pedersen
- Published: Jul 1, 2026

Databricks Forward Deployed Engineering introduces Decision Execution Platforms (DEPs), an enterprise analytics category intended to connect KPI signals, executive decisions, operational execution, and measured outcomes. The proposal addresses workflows in which dashboards reveal problems but meetings, decks, spreadsheets, and messaging threads leave implementation fragmented and impact measurement disconnected. DEPs run the four-stage loop on governed Databricks infrastructure: agents recommend actions, alternatives, predicted impact, and reasoning; approved choices execute through systems of record; and results persist in a Decision Log for continuous learning. Their architecture combines a foundation of Lakebase, Genie, Unity Catalog, Lakehouse, Agent Bricks, and MLflow with an SDK of reusable primitives and a Databricks Apps executive surface. A retailer case used a DEP to unify fulfillment data and enable simulated, controlled rerouting, with scaling aimed at measurable bottom-line and customer-satisfaction outcomes.


### [From monolith to Lakebase to LTAP: rethinking the database from storage up](https://yomu.fyi/post/from-monolith-to-lakebase-to-ltap-rethinking-the-database-from-storage.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Reynold Xin
- Published: Jun 30, 2026

The post examines why monolithic OLTP databases become fragile and difficult to scale when compute, the write-ahead log (WAL), and data files share one machine. Using Postgres as its primary example, it contrasts local WAL durability and physical cloning with Lakebase, whose stateless Postgres compute externalizes those components into SafeKeeper and PageServer services. SafeKeeper replicates log records across a quorum through Paxos-based network replication, while PageServer applies the WAL and materializes data in cloud object storage. The resulting separation is presented as a way to improve durability, independently scale reads and writes, isolate workloads, and support high availability and branching without physical database clones. LTAP extends the design by making transactional tables directly queryable for analytics from a single governed copy, avoiding CDC or mirroring and allowing transactional and analytical engines to scale independently.


### [What Is Serverless PostgreSQL?](https://yomu.fyi/post/what-is-serverless-postgresql.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 25, 2026

Serverless PostgreSQL is presented as a fully managed cloud database model that decouples compute and storage, allowing each to scale independently with demand. Traditional deployments require teams to size infrastructure, manually manage scaling, and absorb costs from idle capacity. In serverless systems, the provider provisions compute on demand, can suspend it when idle, and bills according to active usage; scale-to-zero may introduce cold-start latency. The architecture can also support database branching through copy-on-write, creating isolated environments without duplicating data. The article distinguishes this model from lakebase architecture, which combines transactional and analytical workloads on a shared foundation using decoupled compute, durable object storage, log-based storage systems, and orchestration.


### [The Rise of Sports Intelligence: How the Lakehouse Turns Tracking Data into Competitive Advantage](https://yomu.fyi/post/the-rise-of-sports-intelligence-how-the-lakehouse-turns-tracking-data.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Corey Abshire, Kush Patel, Nick Ragonese
- Published: Jun 24, 2026

Professional basketball’s Hawk-Eye SkeleTRACK feed produces roughly 22,620 positional updates per second—about 65 million records per 48-minute game—yet teams often cannot turn that volume into timely, trusted decisions. The integration gap comes from separate vendors for tracking, wearables, video, scouting, and medical data, alongside calibration differences, weak provenance, and compute limits. The Databricks Data + AI Platform is presented as a governed lakehouse that ingests feeds with Lakeflow, refines them through medallion layers, and uses Unity Catalog for lineage, access control, and auditing. Models for shot probability, injury risk, and fatigue can run alongside serving and custom applications, with Lakebase supporting sub-second interactive queries. Applications include proactive load management, real-time coaching intelligence, and enriched broadcast or fan experiences across tracking-rich sports.


### [What if the answer was already in your data?](https://yomu.fyi/post/what-if-the-answer-was-already-in-your-data.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Bryan Smith, Morgan Wilkie, Kaitlin Ryan
- Published: Jun 24, 2026

Kythera Labs is building an AI-native healthcare strategy platform on Databricks to give health systems access to strategic intelligence that historically required specialized analysts or consulting firms. Its foundation converts 339 billion medical and prescription claims covering more than 300 million patients into governed, event-based data, resolving providers, harmonizing codes across 130 vocabularies, and reconstructing patient journeys. Healthcare Strategy Agent, built with Agent Bricks, lets executives ask questions such as where oncology referrals are going and receive analyses of leakage, competing providers, physicians, and reimbursement opportunity in minutes. A Louisiana health system went live within ten days and reported 150% greater visibility into encounters, 12% more keepage, 22% less leakage, and $3.8 million in estimated annualized retained-encounter value. Unity Catalog, Lakebase, Delta Lake, Delta Sharing, and serverless infrastructure provide shared governance, lineage, access controls, and operational integration.


### [What’s coming next to Free Edition](https://yomu.fyi/post/what-s-coming-next-to-free-edition.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Will Valori, Trang Le
- Published: Jun 17, 2026

Databricks is expanding Free Edition, launched twelve months ago to help people learn modern data and AI, after providing more than $10M in credits to over 500,000 learners. The five additions are Genie Code, serverless GPUs subject to availability, Lakebase, Agent Bricks, and Lakeflow Designer. Genie Code can generate, run, interpret, and refine code autonomously for tasks such as dataset analysis, pipeline cleaning, and visualization, while GPUs support deep-learning workloads including training, fine-tuning, inference, and large-dataset processing. Lakebase adds a fully managed Postgres-compatible database for data apps and AI agents; Agent Bricks supplies composable tools, memory, orchestration, and evaluation; and Lakeflow Designer enables visual pipeline construction. Together, these additions are presented as a complete free toolkit covering data engineering, analytics, machine learning, app development, and AI agents, allowing users to build end-to-end data and AI projects in Free Edition.


### [What’s new in Databricks Data + AI Platform security and compliance at Data + AI Summit 2026](https://yomu.fyi/post/what-s-new-in-databricks-data-ai-platform-security-and-compliance-at-d.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Jason Wu, Samrat Ray, Filippo Seracini, Alex Esibov, Vijay Raja, Kelly Albano, Robert Zhang, Mia Penfold Lopez
- Published: Jun 17, 2026

At Data + AI Summit 2026, Databricks announced security and compliance capabilities for scaling Genie, Lakebase, serverless workloads, and AI-powered applications without relying solely on manual provisioning, static network controls, or siloed compliance programs. Automatic Identity Management (AIM) for Microsoft Entra ID is generally available on AWS and Google Cloud, AIM for Okta is in Public Preview, and Context-Based Ingress is in Public Preview across all three clouds for policies based on network source, identity, and access scope. Private Network Gateway, in Private Preview on Azure Databricks, provides one secure connection from serverless workloads to private networks, while expanded Private Link support extends to Lakebase and other services. Compliance additions include Azure Serverless coverage, HITRUST across AWS, Azure, and Google Cloud, ISMAP on Azure and AWS, expanded AWS GovCloud availability, and planned FedRAMP High support on Azure Commercial.


### [What’s New in the AI Platform: Agents for ML Engineering, Our Deep Learning Platform, and New Capabilities for Real-Time ML](https://yomu.fyi/post/what-s-new-in-the-ai-platform-agents-for-ml-engineering-our-deep-learn.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Tejas Sundaresan, Mike Del Balso
- Published: Jun 17, 2026

The announcement presents three additions to the Databricks AI Platform: Genie Code support for ML engineering, AI Runtime’s serverless GPU environment, and expanded real-time ML capabilities. Genie Code integrates with Unity Catalog, Feature Store, training, serving, monitoring, and MLflow, assisting with feature engineering, model training, deployment, evaluation, and production operations. AI Runtime provides on-demand serverless NVIDIA A10 and H100 GPUs, supports high-performance multinode training with RDMA and high-performance data loading, and adds Lakeflow Jobs, DABs, MLflow, and Unity Catalog integration. For real-time ML, the platform adds declarative feature engineering, streaming features, online feature serving on Lakebase, and enhanced Model Serving targeting 300K+ QPS with under 10ms p99 latency overhead. Reported customer examples include faster workflows, lower infrastructure costs, and production scaling beyond 100K QPS.


### [Sciene AI Companion: building an autonomous Customer Success platform on Databricks](https://yomu.fyi/post/sciene-ai-companion-building-an-autonomous-customer-success-platform-o.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Renata Fencz, Solano Campos, Rodrigo Mohr, Ricardo Morandini
- Published: Jun 16, 2026

Sciene built AI Companion for Quartile’s Customer Success organization, where CSMs support more than 1,000 brands and previously spent substantial time preparing decks, reconstructing context, and investigating account changes. The platform addresses personalization at scale, high-volume content generation, and root-cause diagnosis by combining account data, CSM communication styles, company principles, and cross-domain business data. Its Email Hub cuts reply time from 15–30 minutes to about three minutes, Meeting Hub reduces preparation for 80+ slide decks from over two hours to around 10 minutes, and Account Flagging reduces diagnosis of flagged accounts from 30+ minutes to about five. Databricks provides the shared governed foundation: Delta Sharing supplies data without copies, Lakebase stores operational state, and SQL Warehouses serve analytical, AI, and operational workloads from the same tables. The design keeps CSMs responsible for judgment while giving them current context for customer interactions.


### [Announcing Lakebase Search: agent-native retrieval built into Lakebase Postgres](https://yomu.fyi/post/announcing-lakebase-search-agent-native-retrieval-built-into-lakebase.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Pranav Aurora, Zhou Sun, Jinjing Zhou
- Published: Jun 16, 2026

Lakebase Search is a beta offering on AWS and Azure that adds hybrid vector and full-text retrieval to Lakebase Postgres. It uses the lakebase\_vector and lakebase\_text extensions to keep retrieval, memory, operational data, and hybrid search in one backend. lakebase\_vector retains pgvector types and operators, applies RaBitQ clustering and compression for 32x smaller indexes, and targets more than 1B vectors, while lakebase\_text replaces GIN with object-storage-optimized BM25 ranking. A tiered cache keeps hot data on NVMe and places colder data in object storage; the source reports lower memory needs, faster index builds, and cold-cache startup than standard pgvector HNSW in its LAION-100M benchmark. The extensions also combine vector similarity and keyword relevance with reciprocal rank fusion in a single SQL query, enabling joins and tenant filtering alongside transactional workflows.


### [From Wall Street to Data Platforms](https://yomu.fyi/post/from-wall-street-to-data-platforms.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Andrea Fernández, Kim Hatton
- Published: Jun 13, 2026

Kim Hatton, Databricks’ Global Financial Services Marketing Leader, describes how two decades in regulated financial-services marketing led her toward technology and data-centered strategy. She says financial institutions still need to unlock value while navigating compliance, but their divisions and systems make a unified customer view difficult. Her account points to Unity Catalog’s unified governance and single source of truth for breaking down silos and supporting requirements including GDPR, customer identity, and sensitive workloads. It also describes Lakebase’s separation of compute and storage for faster ML/AI agent experimentation, alongside Genie’s plain-language data analysis, which can reduce work that otherwise takes months and expensive third parties. Hatton connects these tools with faster, more confident marketing and accurate decision-making in regulated workflows, while also describing Databricks’ inclusive, high-energy culture.


[Older posts](https://yomu.fyi/topic/lakebase/page/2.md)