Databricks

Mirakl, a leading provider of eCommerce software solutions, needed a way to bring its data teams onto a single platform and avoid siloed workflows while maintaining control over data. By standardizing on Databricks, it created a single source of truth and built Catalog Transformer, a GenAI system that automates supplier catalog onboarding. What once took about 28 days now takes less than 24 hours, reducing operational costs, improving data quality, and enabling teams to work from a shared, governed environment. Explore Mirakl’s full transformation: https://lnkd.in/g-pw7eWP

Nick Karpov and Holly Smith walk through some of the latest Databricks features - and how they work together under a single architecture. Our R&D teams have been busy. Recent updates include: - Three new foundation models in Databricks Foundation Model API support - Stateless streaming performance improvements - MLflow traces syncing into Unity Catalog tables - Multi-statement transactions, supervisor agents, and metric views Watch the full episode: https://lnkd.in/gK5gmFJK

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 https://lnkd.in/gKctMcpr

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 https://lnkd.in/g-7xMBd9

The DevRel track at Data + AI Summit 2026 is designed for builders. ���️ A multi-day hackathon where teams build agentic data apps for social impact using Databricks Apps, Lakebase, and Agent Bricks ✔️ DevHub Expo with interactive demos, technical showcases, and community programming ✔️ The Grounded Reasoning Cup where AI agents race to reason over the real-world enterprise Plus a DevConnect Meetup, sessions, and networking with developers and startups building the next wave of agentic systems. Save your spot: https://lnkd.in/gd_2Kw76

Data warehouse migrations are often slowed down by the wrong assumptions. Focusing only on cost, treating it as SQL code conversion, or migrating all legacy objects can increase cost, extend timelines, and carry forward unnecessary technical debt. The reality is different. Value comes from: • Platform consolidation • Enabling AI on governed data • Decommissioning legacy systems faster Explore the 10 data warehouse migration myths and best practices: https://lnkd.in/e6XfAXc8

Intent HQ is helping marketers move from weeks of audience segmentation and finding opportunities to meet their goals, to real-time campaign execution based on highest impact opportunities. With its IntentOne agentic framework, campaign managers can autonomously create, test, and optimize thousands of campaigns and opportunities, continuously adapting based on customer response and campaign performance. Databricks Lakebase and Unity Catalog keep everything in one environment, so agents can share insights while data stays secure and within the organization, with AI in a fully airgapped, compliant, and safe environment. https://lnkd.in/gWktPVaq

[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