Three beginner-friendly analytics projects you can finish in an afternoon with Databricks Free Edition • Analyze a simulated environment using AI/BI Dashboards and AI_Query()
• Explore sample trends and generate predictions with AI_Forecast()
• Load open data with Python and
@databricks
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Beginner Analytics Projects with Databricks Free Edition
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Genie Code: Autonomous AI Partner for Data Analysis
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[DEMO] Genie Code is your autonomous AI partner for data work.
— Databricks (@databricks) 31 mars 2026
Starting from a single prompt, watch as it explores datasets, trains and evaluates models, builds a Lakeflow Spark Declarative Pipeline, and creates an AI/BI dashboard – all while maintaining enterprise context.… pic.twitter.com/tgdNAgV8Ke[DEMO] Genie Code is your autonomous AI partner for data work. Starting from a single prompt, watch as it explores datasets, trains and evaluates models, builds a Lakeflow Spark Declarative Pipeline, and creates an AI/BI dashboard – all while maintaining enterprise context.
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Databricks Named #2 in 2026 Enterprise Tech 30 List
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The 2026 Enterprise Tech 30 list is out, and Databricks has been named #2 in the Giga Stage! Over 90 leading VCs and corporate development leaders selected the #ET30, recognizing the top private companies shaping enterprise technology and transforming the future of work. Thank
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AI Agents Transform Database Architecture Beyond 1980s Models
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The database architecture that made sense in the 1980s doesn't hold up in a world where agents are the primary builders. The reason is that agentic development doesn't work like traditional development. AI agents now create roughly 4x more databases than human users on Lakebase.
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Building Real-World AI Agents and Data Applications at Databricks
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Get hands-on with what it takes to build and ship real-world data apps and AI agents.
— Databricks (@databricks) 30 mars 2026
Join us at Databricks AI Days to learn how to:
• Build AI agents that are accurate, grounded in your data, and designed to work in real scenarios
• Develop data apps with a fully managed… pic.twitter.com/Z84KxPROpHGet hands-on with what it takes to build and ship real-world data apps and AI agents. Join us at Databricks AI Days to learn how to:
• Build AI agents that are accurate, grounded in your data, and designed to work in real scenarios
• Develop data apps with a fully managed -
Running Operational Workloads with Lakebase, Databricks Apps, Agents
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See how to run operational workloads on the lakehouse using Lakebase, Databricks Apps, and Agent Bricks. This BrickTalks session covers how teams are building data apps and AI agents on top of serverless Postgres to automate workflows and make data usable in real applications.
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Data Engineering Guide: Build Scalable Pipelines for AI
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Data engineering is getting more complex, but it doesn't have to slow you down. The Big Book of Data Engineering is a practical guide packed with how-tos, code snippets, and real-world examples to help you build and scale pipelines faster and deliver high-quality data for AI,
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Databricks Learning Festival: Certifications and Academy Labs Discounts
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The Databricks Learning Festival is underway!
— Databricks (@databricks) 28 mars 2026
Complete all modules in at least one self-paced learning pathway in Customer Academy by April 3 to earn:
• 50% off any Databricks Certification
• 20% off a yearly Academy Labs subscription
Take advantage of this chance to build… pic.twitter.com/cApwwY6yZsThe Databricks Learning Festival is underway! Complete all modules in at least one self-paced learning pathway in Customer Academy by April 3 to earn: • 50% off any Databricks Certification • 20% off a yearly Academy Labs subscription Take advantage of this chance to build
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LLM-Based Sensitive Data Classification and Drift Detection
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As schemas evolve, keeping sensitive data correctly labeled gets harder. At Databricks, LogSentinel uses LLMs on Databricks to classify columns, apply hierarchical and residency-aware labels, and continuously detect drift, creating tickets for violations. On 2,258 samples, it
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Database maintenance windows cause more disruption than hardware failures
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Planned maintenance causes more database disruption than actual hardware failures.
— Databricks (@databricks) 27 mars 2026
Most databases get patched far more often than they experience outages, but every patch means a maintenance window, severed connections, and a cold cache that tanks performance for minutes after… pic.twitter.com/l1Mgm1PoZOPlanned maintenance causes more database disruption than actual hardware failures. Most databases get patched far more often than they experience outages, but every patch means a maintenance window, severed connections, and a cold cache that tanks performance for minutes after