Databend

Agent trace is not traditional observability trace. Traditional trace: seconds/minutes, stable schema, latency/status/error. Agent trace: minutes/hours, nested JSON, schema drift, tool calls, model outputs, token cost, branch points. That changes the data layer. You need object storage, semi-structured JSON, search, governance, and incremental aggregation. Read the full blog to see how Databend powers agent-ready trace analysis at scale: bit.ly/4v7Ddno #DataInfrastructure #AgenticAI

Immersive Translate needed product analytics without a heavy big data stack. So they kept it simple: 📥 NDJSON on S3 ⚙️ Databend Cloud scheduled Tasks 📊 SQL analytics + BI dashboards 💰 elastic warehouses with auto-suspend 🚀 POC completed in one afternoon. Read the customer story: bit.ly/4bw6EZn #DataEngineering #CloudDataWarehouse

AI can generate an AST Visitor migration fast. Proving it didn’t change SQL semantics is the hard part. Inside Databend: source-derived AST metadata, runtime traces, independent comparison, real SQL corpora, and coverage feedback. Read the blog👉 bit.ly/4wGL6RX #Database #AI

File ingestion gets messy fast: retries, duplicate loads, raw-file replay. A cleaner pattern: 🛠️ Airflow orchestrates the workflow 🗂️ S3 keeps raw files replayable ⚡ Databend Cloud loads with COPY INTO Runnable Airflow DAG included: bit.ly/4vSjDN3 #DataEngineering #ApacheAirflow

Small bitmaps are cheap. Until you aggregate millions of them. In Databend, HybridBitmap keeps small sets lightweight and only switches to RoaringTreemap when the set actually grows. The result: up to 80% faster bitmap aggregation in small-bitmap-heavy workloads. Read the blog 📝bit.ly/4aH0cyt #DataEngineering

@soda_deleted Thank you for the reminder☺️

@DatabendLabs Hi, I noticed a small typo in your info: the correct link should be "github.com/databendlabs", not "github.com/databend".

Agent trace is not traditional observability trace. Traditional trace: seconds/minutes, stable schema, latency/status/error. Agent trace: minutes/hours, nested JSON, schema drift, tool calls, model outputs, token cost, branch points. That changes the data layer. You need object storage, semi-structured JSON, search, governance, and incremental aggregation. Read the full blog to see how Databend powers agent-ready trace analysis at scale: bit.ly/4v7Ddno #DataInfrastructure #AgenticAI

How do you track P50/P95/P99 latency without burning memory? In Databend/OpenRaft: ~2KB histogram, O(1) recording, <0.2% error in typical latency cases. A nice deep dive into the tradeoffs between memory, write overhead, and percentile accuracy in real database internals. For distributed systems engineers: bit.ly/4wjEPLI #AIInfrastructure #DataEngineering

LLM training generates mountains of data. But how do you trace what actually happened? Trace is not just logs. It is evals, feedback, and training signals: prompts, tool calls, tokens, outputs, cost, latency. Agent traces need a queryable data platform like Databend. Already adopted by leading AI labs and foundation model companies. Explore more in the demo🧐 bit.ly/4eF5Nrn #DataEngineering #AIEngineering

Agent performance is not just model performance. Same task: Claude Code + Opus 4.6: ~3 min Claude Code + DeepSeek V4 Pro: ~15 min The difference is not only the model. It is model + harness fit. Tool names, prompts, planning behavior, and runtime conventions shape the path. Final pass/fail won’t show this. The trace will. That’s why Databend is built for agent trace storage, search, and incremental aggregation. Get free credits and try Databend Cloud: app.databend.com/login #AIEngineering #DataArchitecture

Most warehouses let you mask a column. But what about a single key inside a JSON column? Databend's VARIANT sub-field masking lets you selectively hide JSON keys (like secret_key, token) based on role — while the rest of the object stays queryable. Why this matters for AI observability & eval systems (like Langfuse, LangSmith) → One JSON column, multiple sensitivity levels → Role-based visibility at the key level, not just column level → All access paths respect the mask: subscript, json_path_query, cast, json_object_keys No data duplication. No schema redesign. Just policy. #masking-variant-sub-fields" target="_blank" rel="nofollow noopener">docs.databend.com/guides/securit…

How does Databend Cloud scale on 1TB of TPC-H data? • Small: 1173s • Medium: 538s (2.18x faster) • Large: 286s (4.10x faster) 6 billion rows, 22 analytical queries, predictable performance scaling docs.databend.com/guides/benchma…

We just shipped llms.txt for Databend — a progressive skill system for your AI agent. Fetch docs.databend.com/llms.txt for Databend context One line. Your agent gets a structured index of everything Databend — SQL, data loading, tuning, troubleshooting. From there, it progressively fetches deeper docs (sql.txt, guides.txt, tutorials.txt) only when needed. No token waste. Pair it with our MCP server and your agent goes from knowing Databend to acting on your data — query, explore, build — all safely sandboxed. → docs.databend.com/llms.txt → databend.com/mcp/

New in #Databend: Geospatial goes deeper 🌍 - Geometry aggregate funcs in SQL - Refreshable Spatial Indexes - ST_DWITHIN-powered index pruning - Geo values encoded properly in Arrow results Less full-scan geometry. More pruning. Faster spatial queries. Built in Rust, running on your object storage. github.com/databendlabs/d…

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-…