Databricks Apps
Recent items mentioning Databricks Apps across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
Databricks Apps is evolving into an enterprise app platform focused on secure, governed AI application development, introducing App Spaces, serverless micro apps, and Genie App Builder for production-quality apps 1. Community discussions highlight practical aspects like automatic Git deployments 4, cost optimization through scheduled jobs 5, and monitoring Databricks Jobs within custom apps 78. There's also interest in comparing Databricks Apps with other platforms like ServiceNow 2.
Generated daily from the 8 most recent items mentioning Databricks Apps. Click any [N] to jump to the source.
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.
Databricks apps versus ServiceNow apps
Automatic Git Deployments for Databricks Apps from Github
Cut our Databricks Apps costs by 76% with two scheduled jobs (start/stop)
Databricks apps
How to create Databricks Jobs Monitoring in your custom Databricks App
Databricks Jobs Monitoring in Your Custom Databricks App
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.
