Databend

🚀 #Databend evolved into a 𝙪𝙣𝙞𝙛𝙞𝙚𝙙 𝙢𝙪𝙡𝙩𝙞𝙢𝙤𝙙𝙖𝙡 𝙙𝙖𝙩𝙖𝙗𝙖𝙨𝙚 Pure #Rust kernel = blazing performance & safety One Snowflake-compatible SQL interface seamlessly handles: • BI Analytics - Traditional SQL workloads • AI/Vector Search - Embeddings & semantic search • Full-text Search - JSON queries • Geospatial Analytics - Location-based insights All workloads share the same query optimizer & elastic runtime = unified efficiency 🔥

New in #Databend: READ_FILE Load any file from a stage directly in SQL. ```sql SELECT READ_FILE('@stage', file_path) FROM documents; ``` Images. PDFs. Binaries. No pipeline. No preprocessing. Just SQL. Docs: docs.databend.com/sql/sql-functi…

We just shipped .md support across all Databend Docs ⚡️ Every guide is now available as clean, raw Markdown — perfect for AI agents, RAG pipelines, and LLMs. No more HTML parsing. Just add .md and go. Try it: → docs.databend.com/guides/ai-func… → docs.databend.com/guides/ai-func… AI devs building on Databend, this one’s for you.

Unlock the power of Databend's new async read_file function! 📂 Read any file from object storage directly into binary columns—perfect for AI workflows: load PDFs, images, or docs, then parse and analyze with your favorite libraries. Example: read_file('@my_stage/docs/report.pdf') → feed to AI parsers for insights! Dive in: github.com/databendlabs/d…

Databend January 2026 Monthly Report is here! 🚀 - Experimental Table Branching & Tags: Create safe data sandboxes for AI agents with easy DDL, isolation, and snapshot-based time travel. - Spatial Indexes: Full Geometry/Geography support + CREATE/DROP SPATIAL INDEX for blazing-fast queries. - Iceberg Write Support: Upgraded to v0.8.0 for complete write paths and open data lake compatibility. - Query Perf Boosts: Self-join elimination, lazy joins, Parquet optimizations, and high-speed bitmap operations. - Meta Enhancements: Better observability with histograms, faster KV scans, and cluster stability refactors. 11 nightly releases packed with 33 new features and 31 fixes—focusing on governance and speed. Dive in: databend.com/blog/category-…

Excited to explore the future of Agent-Ready Databases—empowering AI agents in enterprise data ops with seamless, secure workflows! Our vision at Databend includes: - Unified semantic storage: Seamless SQL access to structured, unstructured, and vector data—with rich metadata for instant context - Instant schema evolution: Zero-cost changes like adding columns, no data rewrites - Git-like branching: Safe, zero-overhead snapshots for isolated testing and risk-free merges - Robust ACID transactions: Atomic multi-step operations with easy rollbacks for self-correcting agents - UDF Sandbox (coming soon): Isolated Python execution for AI logic, minimizing data risks - Extreme stability: Auto-scaling under high loads for 24/7 agent reliability We're advancing with native Parquet support, metadata versioning, zero-copy snapshots, multi-statement ACID, and more—shaping the next-gen warehouse. Dive in: databend.com/blog/category-…

🚀 We just shipped #Databend MCP: Your safeguard for AI-driven data ops in production. Read-only access meets isolated sandboxes—zero risks, all the power. Key features: • Prod read-only: SELECT/SHOW on live data, no writes • Per-session sandbox: Safe writes in unique namespaces • Verification magic: Sample, simulate, validate queries Integrate with Codex CLI, Claude, Cursor via DSN. Dive in: databend.com/blog/category-… Repo: github.com/databendlabs/m… Docs: databend.com/mcp/

Snowtree GitHub repo: github.com/databendlabs/s…

Introducing Snowtree: Databend's production-proven framework for integrating LLMs into large-scale Rust monorepos. Leverage isolated Git worktrees for safe AI-assisted code generation and refactoring. Execute via raw CLI with models like Codex, Claude Code, and Gemini; enforce rigorous line-by-line reviews to maintain code integrity, mitigate technical debt, and ensure human oversight. Derived from our 1.9M LoC cloud-native data warehouse engineering practices. Explore the details: databend.com/blog/category-…

The architecture:

🚀 Introducing Table Branching in Databend — Git-like version control for your data warehouse tables. Create isolated branches for development, testing, and safe AI agent experiments; tag important snapshots; and query any point in time — all while sharing underlying storage. Here's how it works:

Been waiting to write to Iceberg tables in #Databend? v1.2.867 has you covered: ✓ Full INSERT support ✓ Partition-aware writes ✓ Transaction guarantees ✓ All catalog types supported Check the release notes for details: github.com/databendlabs/d…

Excited to ship branch-aware INSERTs in #Databend! Now you can target specific branches like: ``` INSERT INTO table_name/branch_name ... ``` Big flex for data management. Check it out: github.com/databendlabs/d…

🚀 #Databend Ingestion Benchmarks: Scaling from 3K to 2.3M rows/s! We tested four ingestion methods—Bulk INSERT (with/without presign), STREAMING_LOAD, and STAGE_LOAD (COPY INTO)—in cloud and on-premises environments. The results: Dramatic throughput gains, improved cost efficiency, and insights into the best approach for your workload. Dive into the full analysis: databend.com/blog/category-…

#Databend 2025 recap: Unified columnar OLAP engine evolves into a full-stack Analytics + Search + AI powerhouse on object storage. ⚡ Key evolutions: • Petabyte-scale datasets handled via SQL-driven ETL and stream ingestion • Blazing OLAP aggregations without data movement • Full-text/JSON inverted indices for advanced search • Vector embeddings powering RAG workflows • GEO spatial indexing for location-aware queries • And more—no silos, zero shuffling Databend Cloud reaches GA; #Databend v1.3 coming soon with fused optimizer, execution runtime, and storage engine for sub-second latencies at massive scale. Deep dive 👉 databend.com/blog/category-…

2025 #Databend Year in Review: 🚀 188 releases shipped, ⭐ 9.1K GitHub stars, 👥 248+ contributors. Key Highlights: • Evolved from warehouse to analytics + search + AI platform for complex queries. • Databend Cloud GA: Powering global production at massive scale. Wrapping up v1.3: Goal—simplify AI apps on petabyte data. Looking to 2026: • Graph data for AI reasoning chains and relational insights. • End-to-end: Storage, analytics, search + inference on huge datasets. • AI Flow: Effortless pipelines blending massive data with AI. Databend Cloud bundles it all user-friendly. Dive in: databend.com 🎉

As 2026 approaches, we're thrilled to unveil the revamped #Databend website! 🚀 Dive into our latest features for analytics, search, and AI in one powerful warehouse. databend.com

Thrilled with #Databend's optimizer and executor upgrades—months of team effort shining through! Outperforming #Snowflake on TPC-H SF1000: same dataset, same queries, no tuning—206.73s vs. 228.71s. Query breakdown below. Plus, we're making comprehensive improvements to full-text indexing and vector search. Open-source speed wins! 🚀 github.com/databendlabs/d…

Introducing MCP for #Databend! ⚡ Connect AI agents like Codex, Cursor, Claude Code, and Gemini CLI to your Databend databases with full schema context. Empower them for: 🚀 Lightning-fast SQL generation from natural language – 10x quicker than traditional methods 🔍 Deep schema exploration, instant query validation, and error detection 🔧 Zero-config ETL automation via COPY, MERGE, and Stage ops Safe mode blocks destructive actions. Ideal for devs and data teams. Get started: databend.com/mcp/

New in #Databend: Tag support for powerful data governance. 🚀 Attach key-value labels to databases, tables, stages, and connections to organize and track data effectively. Key benefits: - Compliance: Label sensitive data (e.g., sensitivity = 'confidential' or regulation = 'GDPR') for audits and policy enforcement. - Ownership: Assign owner = 'data-team' or cost_center = 'engineering' for accountability and cost allocation. - Environments: Differentiate env = 'prod' from 'dev' to prevent mix-ups in complex setups. PR (Phase 1): github.com/databendlabs/d…

How does #Databend keep ETL pipelines running through schema changes in Parquet files — without downtime or data breaks? 🔥 github.com/databendlabs/d… ⭐ Basic schema evolution in COPY INTO for Parquet ⭐ Add new columns seamlessly ⭐ Type conversions where possible Uninterrupted data flows for evolving schemas. Landing soon. 🚀