Databend Cloud | Data Agent-Ready. One Warehouse. Blog
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A cluster key is neither a B-tree index nor a set of independent access paths for individual columns. It improves physical data locality so that similar key values tend to land in the same or adjacent micro-partitions or blocks. This narrows each physical unit's Min/Max range, reduces overlap, and allows the engine to prune more data before reading it.
This post is written for data engineers building Agent Trace pipelines, log analytics systems, Kafka-to-warehouse ingestion, or high-throughput semi-structured data platforms. It explains how Agent Trace data flows from Kafka into Databend Cloud, and how the open-source project [bend-ingest-kafka](https://github.com/databendcloud/bend-ingest-kafka) is designed for this workload.
From Kafka ingestion and schema-flexible raw storage to incremental processing, Evals, and governed analytics for million-token AI workloads.
Over June and July 2026, Databend shipped 182 changes across query optimization, spatial analytics, open lakehouse integration, data pipelines, stability, and security.
This article explains how Databend combines KLL, Top-N, and Count-Min Sketch (CMS) to build optimizer statistics with lower `ANALYZE` overhead. Together, they balance histogram efficiency with accurate estimates for skewed and high-frequency values.
`QUALIFY` does more than remove a layer of nesting. It keeps window-function logic and the filter applied to its result in the same semantic stage, making analytical SQL easier to read, review, and maintain. This article covers six practical patterns, including event deduplication, Top N queries, CTE boundaries, and named windows.
Immersive Translate is a popular bilingual translation extension used to translate web pages, PDF documents, ePub books, subtitles, and more. The product supports a wide range of translation providers, including Google, OpenAI, DeepL, Microsoft, Gemini, Claude, and others, giving users the flexibility to choose the service that best fits their workflow.
An AST Visitor API migration is easy for AI to generate but hard to trust. This article explains how we validated that a new traversal preserved SQL semantics using source-derived AST metadata, runtime traces, independent comparison, diagnostics, real SQL inputs, and coverage feedback.
This post walks through a minimal Airflow pipeline that uploads local CSV or NDJSON files to S3, then loads them into a cloud data warehouse with `COPY INTO`. The pattern keeps orchestration in Airflow, raw files in object storage, and ingestion plus analytics in the warehouse layer. It also gives you a practical starting point for retries, replay, credential handling, and production hardening
TL;DR: Databend introduces HybridBitmap to keep small sets lightweight and switch to RoaringTreemap only when needed. This reduces unnecessary deserialization and allocation, improving bitmap aggregation performance in small-bitmap-heavy workloads.
