Databricks Apps
Recent items mentioning Databricks Apps across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
Developers can now construct and deploy Databricks Apps directly by feeding copyable Developer Hub prompts into AI coding assistants like Cursor 9 or Replit 10. In production, pairing Databricks Apps with Lakebase Postgres and route-optimized Model Serving yields p50 latencies of 27 ms under 5,000 requests for real-time fraud scoring 8, enabling enterprise deployments like NorthStar Anesthesia's clinician scheduling tool built for 3,000 workers 4.
Generated daily from the 10 most recent items mentioning Databricks Apps. Click any [N] to jump to the source.
NewsBuilding Agents on Databricks with Custom Apps and Omnigent
This video demonstrates how to build, update, and govern custom AI agents on Databricks using Agent Bricks, Databricks Apps, and Omnigent. The tutorial shows how to integrate Model Context Protocol servers, track execution with MLflow traces, schedule automated agent tasks, and manage security policies through Unity AI Gateway.
TutorialsDetect Energy Theft Faster with Genie
Databricks demonstrates an end-to-end AI application that detects energy theft, automates investigations, and generates executive reports using Unity Catalog and Genie. The video walks through an architecture featuring Lakebase for transactional storage, model serving for machine learning and LLMs, and AI gateways for governance and cost control.
Databricks Apps - "App Not Available" error with locationId parameter missing
How NorthStar Anesthesia built a scheduling app for a workforce of 3,000 clinicians in weeks
NorthStar Anesthesia built and shipped a custom scheduling app for 3,000 clinicians in just weeks using Databricks Apps. By leveraging their existing security and data infrastructure,
Databricks Apps (Streamlit) - How to Implement Proper Logout Functionality?
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
Databricks Marketplace: IP Protection, Job Compute, and Secret Management
I am planning to publish an application through Databricks Marketplace. The application contains proprietary business logic and processing algorithms that must not be accessible to customers after installation. Customers should be able to use the application to process data in their own Databricks environment, but they should not be able to inspect, copy, or reuse the underlying implementation. Approaches I Have Tried I have considered the following approaches: Packaging the implementation as Python wheels or other artifacts. Hosting the implementation as private Python packages. Deploying the application source from a private Git repository. With the packaging approach, the underlying Python implementation may still be accessible or inspectable from notebooks, workspace files, cluster environments, package caches, or other customer-accessible locations. This does not meet my IP-protection requirements. With private package hosting, the package installation requires an access token or API key. Providing a reusable package-registry credential to an application running in the customer's workspace creates a risk that the credential could be extracted or reused to download the private package outside the intended application flow. I also understand that Databricks Marketplace reviewers may require the submitted application code to be human-readable during the review process. Therefore, I am looking for an architecture that protects the production implementation without relying only on code obfuscation. Current Understanding My understanding is that a Databricks App runs in an isolated, containerized runtime. This provides separation between the application runtime and normal customer workspace resources. However, my workload includes processing large datasets. I understand that Databricks App compute is primarily intended to run the application, API, or user interface, rather than perform large-scale Spark processing. Because of this, I am unsure whether Databricks App […truncated]
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.
TutorialsHow to Go From an Agent Prompt to a Live Databricks App | Databricks Apps Demo
The Databricks Developer Hub provides copyable agent prompts that developers can paste into coding assistants like Cursor to automatically build, configure, and deploy Databricks applications. Developers can also use specialized template prompts from the hub to add advanced features to their apps, such as integrating a natural language chat interface for querying workspace data.
TutorialsHow to Build Enterprise Apps on Your Databricks Data Using Replit | Databricks + Replit Demo
This video demonstrates how to build and deploy enterprise applications on Databricks using Replit's AI agent and pre-built templates from the Databricks Developer Hub. Viewers learn how to select a template, connect it to a Databricks dataset via Replit, and deploy the finished application directly to Databricks with built-in security and governance.
NewsSolving AI development bottlenecks with Databricks App (with demo)
Databricks Apps provides an enterprise app platform with data security and governance at its foundation, enabling users to build and deploy AI-powered applications that access governed data. The platform introduces App Spaces for scalable governance, serverless micro apps for cost-effective lightweight applications, and Genie App Builder for context-aware, production-quality app development from prototypes.
This release adds an XLARGE compute size option to the Databricks Apps service. It also introduces a meta-harness user-agent dimension that automatically detects and reports the omnigent environment variable.
This release adds a new Xlarge compute size option to the Databricks Apps service. It also introduces a meta-harness user-agent dimension that automatically detects and reports the omnigent meta-harness environment variable.
TutorialsMCP Servers + OBO Auth: The Formula for Context-Aware Agents
The video demonstrates how to build an AI agent in Databricks that provides personalized responses by integrating user-delegated actions through Model Context Protocol (MCP) servers. It walks through setting up Unity Catalog functions, external MCP tools like web search, and custom MCP servers to access internal APIs, all while maintaining user context for relevant information retrieval.
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.
NewsZerobus Ingest, Lakebase and Databricks Apps in Action: Data Streaming with Databricks
The video demonstrates a real-time IoT data streaming application built with Zerobus for ingestion, Lakebase for low-latency serving, and Databricks Apps for the front and back ends. This architecture processes thousands of concurrent IoT events from mobile phone sensors globally without using Kafka or traditional complex pipelines.
TutorialsAir Traffic Control with Apache Spark Structured Streaming Real-Time Mode
The video demonstrates building a real-time air traffic control application using Apache Spark Structured Streaming Real-Time Mode, Lakehouse, and Databricks Apps. This system processes live flight telemetry, detects congestion, and generates alerts with sub-second end-to-end latency, all within a single Databricks platform.
AI App Development: Guide To Building AI-Powered Apps
Databricks Apps and Lakebase are purpose-built platforms that streamline AI app development by eliminating infrastructure, authentication, and data synchronization overhead. A structured process covering model strategy, prompt design, agent orchestration, and data prep, combined with rigorous quality gates, ensures production-grade AI applications.
NewsZerobus Ingest and Lakebase in Action: Data Streaming with Databricks
The video demonstrates a real-time IoT data streaming application built with Zerobus for ingestion, Lakebase for low-latency serving, and Databricks apps for the front and back end, without relying on Kafka. It showcases how thousands of concurrent IoT events from mobile phone sensors worldwide are ingested, processed, and visualized on a map, with traces served by Lakebase for fast access.
NewsDatabricks Apps vs Model Serving: Authentication, Cost, and Performance Compared
Databricks Apps are now the recommended first choice for deploying agents due to their flexibility in handling full-stack applications with multiple components, offering faster iteration and local testing compared to Model Serving. Model Serving remains suitable for use cases prioritizing high QPS, governance features like AI Gateway, inference tables, and guardrails, or when scaling to zero is acceptable for cost optimization.
TutorialsLakebase - OLTP Workloads on Databricks!
Lakebase is a fully managed, serverless PostgreSQL offering from Databricks that decouples compute and storage, enabling independent scaling, auto-scaling to zero, and deep integration with the Databricks Lakehouse. It supports reverse ETL to bring data from the Lakehouse into Lakebase for OLTP applications and forward ETL to sync transactional data back to the Lakehouse for analytics.
NewsLakebase: Postgres That Actually Likes Your Lakehouse
Lakebase is a new Databricks offering that provides a fully managed, autoscaling PostgreSQL database designed to bridge the gap between analytical and transactional workloads in a lakehouse architecture. It features bidirectional data streaming between Delta tables and PostgreSQL, database branching for isolated development, and Unity Catalog governance.
NewsDatabricks News: Excel add-in, Metrics Views UI, and Quality Monitoring
Databricks announced Lake Watch for cybersecurity, new dynamic dropdown filters in SQL editor, and improved quality monitoring with null value scanning and automated alerts. The video also demonstrates a new UI for defining metric views, an Excel add-in for data preview and import, and the ability to publish dashboards as public web pages.
This release adds new resources and data sources for managing Databricks Apps Space and Endpoints. It also updates the underlying Go SDK to version 0.108.0.
NewsDatabricks Breaking News: 2026 Week 6: 2 February 2026 to 8 February 2026
Databricks introduces agentic data quality monitoring with anomaly detection, LLM judge UI builder for MLflow, and new SQL warehouse features including a default option and activity details. The platform also enhances its assistant to connect with MCP servers, improves Google Sheets integration with pivot table functionality, and adds direct Git deployment and tagging for Databricks apps.
NewsDatabricks Breaking News: Week 51: 15 December 2025 to 21 December 2025 #databricks news
Databricks introduces new Lakeflow Connect features, including custom logic for declarative pipelines and new connectors for incremental data import from sources like Confluence, PostgreSQL, and MySQL. The platform also announces the deprecation of legacy features like Hive Metastore and DBFS for new accounts, alongside updates to Lakehouse ACLs, job scheduling from notebooks, flexible node types for cluster deployment, and expanded resource assignment in Databricks apps.
EventsDAIS25 Keynote Day 2 Sizzle
Databricks announced a free edition of its platform, allowing users to access a slice of Databricks forever without a credit card. The company also showcased Agent Bricks for building production-ready AI agents and Databricks Apps for secure data intelligence applications.
