Databricks

[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. https://lnkd.in/gqffbyKv

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 you Wing Venture Capital and Eric Newcomer for this honor. https://lnkd.in/gM_cfBWY

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. Agents spin up databases for experiments, branch them for testing, and discard them when done. About half have a compute lifetime of less than 10 seconds, with high cost sensitivity. Agents also prefer open source tools like Postgres to proprietary databases. They generate queries, schemas, and integrations far more accurately for systems their training data actually covers. Here's what databases actually need to look like in the agentic era: https://lnkd.in/gigxFBJB

As data estates grow, teams struggle to find, understand, and trust the right data. The new Discover experience in Unity Catalog brings business context, trust signals, and access into one place. With domains, intelligent curation, and governed access, users can discover data, analytics, and AI assets aligned to how the business works and move from discovery to action with more confidence. Now in Beta: https://lnkd.in/gnVPY9-S

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. https://lnkd.in/g5MehP8i

Get hands-on with what it takes to build and ship real-world AI, apps and agents. Join us at Databricks AI Days to learn how to: • Build AI agents that are accurate and grounded in your data • Develop intelligent apps with a fully managed Postgres database • Unify data, analytics, and AI with built-in governance Stops ahead: 📍Amsterdam 📍Chicago 📍Washington D.C. 📍Paris 📍Bangalore 📍Sydney 📍Frankfurt & more https://lnkd.in/gJe9XUbJ

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, BI, and analytics workloads. Inside: - Patterns for scaling ETL pipelines effectively - Orchestrating data, analytics, and AI workloads - Implementing observability for your data pipelines - Using Lakeflow to manage pipelines https://lnkd.in/g8kTArb7

Lumen Technologies processes terabytes of telecom data daily across two mission-critical systems supporting billing, network optimization, and regulatory compliance. Their legacy on-premises platform was holding them back. Fragile pipelines, limited governance, and constant manual maintenance meant engineers spent more time firefighting than building. After migrating 133TB to Databricks SQL on Azure, that changed: - Compute costs down 30–40% with serverless scaling - Queries that took hours now run in minutes - 7GB of telecom data processed every 10 minutes https://lnkd.in/gXurtnW4

The 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 real skills in data engineering, analytics, machine learning, and GenAI. https://lnkd.in/eKqQkRqj

Planned 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 restart. We’re changing that. This is Part 1 of our series on eliminating the impact of planned maintenance entirely, rolling out automatically over the next few weeks. https://lnkd.in/gy8_fK3c