Databricks

Today we’re announcing Lakewatch — a new open, agentic SIEM. Security has changed. Attackers now use agents, operating 24/7 at machine scale, while legacy security tools were built for human-speed threats. We need tools where agents work alongside humans to keep up. Lakewatch brings a new architecture for this agentic era: • Ingest and store all enterprise data, including multimodal sources • Analyze it alongside business data with full governance • Use AI agents to automate detection, investigation, and response Security requires a fundamental platform shift. This is how teams can fight agents with agents. databricks.com/blog/databrick…

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. community.databricks.com/t5/learning-ev…

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 achieved up to 92% precision and 95% recall for PII and is now informing Data Classification to improve policy enforcement and compliance workflows. See how: databricks.com/blog/logsentin…

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. databricks.com/blog/zero-down…

We've lots of time building agents at Databricks, and developed this coSTAR pattern for it. It works even better with better models and keeps your agent hill-climbing on the hardest tasks from users without regressing. And it's super easy in @MLflowOSS. databricks.com/blog/costar-ho…

Data engineers want AI in their ETL workflows without adding complexity. With Lakeflow and Agent Bricks AI functions, teams can apply AI directly inside data pipelines to process unstructured data, automate repetitive tasks, and turn raw inputs into usable signals at scale. Practical examples include: • Turning call transcripts into summaries • Automating insurance claims processing from emails, PDFs, and images • Applying AI transformations directly in ETL pipelines databricks.com/blog/ai-first-…

More teams are exploring agentic analytics to simplify how they work with data. Watch @Alex_TheAnalyst and experts from @AnthropicAI and Databricks break down how agentic analytics is changing how teams work, and learn practical skills for: - Understanding how agentic analytics spans from data preparation to insight - What’s new across Databricks analytics tools - Using Genie and Claude to deliver agentic insights - Building end-to-end analytics workflows without BI seats databricks.com/resources/webi…

Stop manually moving data between two databases! Moving data from a production database to a lakehouse is super common, and it's brittle, yet people try to do it themselves by using LLMs or by hand. AutoCDC automates this for you and almost always beats the performance of hand rolling this. Read this blog to understand how this works: databricks.com/blog/stop-hand…

Modern BI wasn’t built for AI. Fragmented tools, duplicated logic and inconsistent metrics make analytics harder to trust. A better approach brings data, semantics, dashboards and AI together on a single governed foundation. This guide explores how modern analytics platforms can: • Simplify analytics architecture • Improve metric consistency across tools • Accelerate real-time insights • Let teams ask questions in plain language and get analytical answers databricks.com/resources/guid…

Databricks has been named to @FastCompany's Most Innovative Companies of 2026! Congratulations to the Bricksters whose work and belief in what we are building made this possible. With recent innovations like Lakebase, Lakewatch, and Genie Code, we’re continuing to push the boundaries of data and AI, and are excited for what’s ahead. Proud to be recognized on this year's #FCMostInnovative list. fastcompany.com/91502052/datab…

Databricks CEO @alighodsi joined @CNBC's @dee_bosa live from #RSAC2026 to talk about the company's entry into cybersecurity with Lakewatch, and what he sees as a fundamental shift in how organizations defend themselves. Attackers are using AI agents to exploit vulnerabilities in a single day, a window that used to be months. "We're fighting agents with agents instead of fighting agents with humans," said Ghodsi. Watch the full conversation: youtube.com/watch?v=M20UjO…

This morning at #RSAC2026, Databricks Co-founder and CEO @alighodsi and @a16z Co-founder @bhorowitz took the stage to make the case for a fundamentally different approach to cybersecurity. AI agents now autonomously read CVEs, construct exploits, and coordinate attacks around the clock. This isn't just about scale, it's about who can attack. As Ben Horowitz put it, "You don't have to be a hacker, you just have to ask an agent the wrong question." The security stack most organizations rely on today was built for a completely different world, and today's tools force defenders to choose what data to keep and what to throw away while attackers use AI to analyze everything. The only viable path forward is to fight agents with agents, with all your data in an open security lakehouse as the foundation. "For the first time in history your ability to investigate vulnerabilities is not limited to the size of your team. We are enabling entirely new methods of defense." — Ali Ghodsi

Our CEO and Co-founder @alighodsi shares why Databricks is entering the cybersecurity market with Lakewatch — a new open, agentic SIEM. "The prevailing pricing model is at odds with protecting against this avalanche that's coming our way, because it's just too prohibitively expensive to get all your data in there." AI has completely changed the security landscape as attackers exploit vulnerabilities at machine speed. Legacy SIEMs create a financial penalty on every byte ingested, forcing teams to drop critical data. Lakewatch unifies security, IT, and business data in a single, governed environment to deploy AI-powered detection and response at petabyte scale and a fraction of the cost. See why organizations like @Adobe and @NAB are already using it. @jordannovet covers the full story for @CNBC: cnbc.com/2026/03/24/dat…

We believe security needs to be open and agent-centric. 𝐋𝐚𝐤𝐞𝐰𝐚𝐭𝐜𝐡 is built directly on the open data lakehouse pattern. By bringing agentic automation directly to where your open data already lives, we're automating the heavy lifting in the Security Operations Center. As a result, you keep ownership of your data, your agents can operate on absolutely everything, and you get it done in a architecturally cost efficient way. databricks.com/blog/databrick…

Databricks enters cybersecurity market with Lakewatch launch, bulking up ahead of IPO cnbc.com/2026/03/24/dat…

Tired of using fragmented architectures for your near real-time apps? Eliminate multi-hop ingestion and complex reverse ETL with a unified end-to-end approach. ✅ Zerobus Ingest: Near real-time ingestion (≤5 seconds) that writes event data directly to governed Delta tables ✅ Lakebase: Fully managed Postgres database built into Databricks for low-latency operational workloads ✅ Databricks Apps: Build and deploy interactive applications within the Databricks Platform Ingest, build, and deploy applications faster on a single data foundation databricks.com/blog/building-…

We're making warehouse migrations faster and more predictable with Lakebridge’s latest AI-powered updates. See what’s new: ➡️ Synapse support in the assessment profiling tool ➡️ AI-powered SQL conversion ➡️ A guided desktop app that brings assessment, code conversion, and reconciliation into one workflow databricks.com/blog/new-migra…

AI coding tools are getting very good at writing code. But data work is not just code. Genie Code is built for what happens after. It can build pipelines and models, then keep them running by monitoring pipelines and AI models, triaging failures, and investigating anomalies. It works with the context data teams actually depend on. So teams can spend less time reacting to issues and more time moving work forward. databricks.com/blog/introduci…

Databricks AI Days are underway, bringing together 15,000+ data and AI leaders to see how modern teams are building and shipping intelligent apps. This year, the spotlight is on Lakebase: a new category of operational database built for the age of agents, where storage and compute are fully separated and serverless Postgres scales instantly with demand. Get hands-on with sessions and trainings on building and deploying AI agents with Agent Bricks, powering those agents with Lakebase Postgres, and querying your data in natural language with Genie: databricks.com/ai-days?region…

When dashboards inform board decks, forecasts, and compensation, small errors can have real impact. This tutorial walks through how to manage Databricks AI/BI dashboards with Git and Asset Bundles so every change is versioned, reviewed, and reversible, with controlled deployment across environments. Follow a step-by-step workflow to keep updates visible, testable, and safe to deploy without changing how analysts work day to day. databricks.com/blog/tutorial-…