Databend

@DatabendLabs

Agent-ready Warehouse for analytics, search, AI, and Sandbox. Databend Cloud: databend.com GitHub: github.com/databendlabs/d…

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Databend
@DatabendLabs
Aug 6
Millions of context tokens. Thousands of tool calls. TB-scale Trace writes per hour. See how a leading foundation model company built an end-to-end Agent Trace pipeline on Databend Cloud—turning raw execution data into eval-ready datasets. Read the customer story 👇
00:00
Databend
@DatabendLabs
20h
🔥We’re launching the Agent Trace Solution. Capture complete agent runs, preserve evolving JSON, and turn new spans into query-ready and eval-ready data—on one S3-backed lakehouse. Built for production scale 🔍 bit.ly/3UIiXMJ
Databend
@DatabendLabs
Sep 3
Async refresh shouldn’t mean stale answers. Databend materialized views use Change Tracking + checkpoints, then choose Fresh, Hybrid read fix, or Live Fallback at query time. Refresh lag changes query cost—not correctness. Deep dive 👉 bit.ly/4xav8Q7
Databend
@DatabendLabs
Sep 1
One lakehouse. Five production scenarios. Agent traces, AI apps, industrial telemetry, public-sector data, and financial archives—built on Databend Cloud with the same pattern: preserve raw data, process changes incrementally, isolate compute. Read 👉 bit.ly/4gDTQlF
Databend
@DatabendLabs
Aug 25
On a controlled 100M-row test: 62.9% → 13.6% of blocks scanned. 63.5M → 13.6M rows read. The difference: a workload-driven Databend Cluster Key. Learn how to choose columns, key order & granularity—and verify the result 👉 bit.ly/4y3VF2k