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What Is a Lakebase?

databricks

Get a Demo DATA + AI SUMMIT JUNE 9–12 | SAN FRANCISCO Data + AI Summit is almost here — don’t miss the chance to join us in San Francisco! Modern development workflow : Branching a database should be as easy as branching a code repository, and it should be near instantaneous. REGISTER Ready to get started?

Database 214
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Introducing Databricks One

databricks

Get a Demo DATA + AI SUMMIT Data + AI Summit Happening Now Watch the free livestream of the keynotes! Join now Ready to get started? 160 Spear Street, 15th Floor San Francisco, CA 94105 1-866-330-0121 See Careers at Databricks © Databricks 2025.

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Introducing Agent Bricks: Auto-Optimized Agents Using Your Data

databricks

Get a Demo DATA + AI SUMMIT JUNE 9–12 | SAN FRANCISCO Data + AI Summit is almost here — don’t miss the chance to join us in San Francisco! REGISTER Ready to get started? This approach democratizes agent development, allowing domain experts to contribute directly to system improvement without deep technical expertise in AI infrastructure.

Analytics 348
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Announcing managed MCP servers with Unity Catalog and Mosaic AI Integration

databricks

160 Spear Street, 15th Floor San Francisco, CA 94105 1-866-330-0121 See Careers at Databricks © Databricks 2025.

AI 205
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Recapping the Cloud Amplifier and Snowflake Demo

Towards AI

Recapping the Cloud Amplifier and Snowflake Demo The combined power of Snowflake and Domo’s Cloud Amplifier is the best-kept secret in data management right now — and we’re reaching new heights every day. If you missed our demo, we dive into the technical intricacies of architecting it below. Instagram) used in the demo Why Snowflake?

ETL 111
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Launch HN: Chonkie (YC X25) – Open-Source Library for Advanced Chunking

Hacker News

We also offer hosted and on-premise versions with OCR, extra metadata, all embedding providers, and managed vector databases for teams that want a fully managed pipeline. or book a demo: https://cal.com/shreyashn/chonkie-demo. 200k+ tokens) with many SQL snippets, query results and database metadata (e.g.

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Tackling AI’s data challenges with IBM databases on AWS

IBM Journey to AI blog

Also, traditional database management tasks, including backups, upgrades and routine maintenance drain valuable time and resources, hindering innovation. By using fit-for-purpose databases, customers can efficiently run workloads, using the appropriate engine at the optimal cost to optimize analytics for the best price-performance.

AWS 93