# Databend Blog > LLM-friendly index of the official Databend blog: 206 posts organized by category. Each category links to an index file listing all of its posts. ## Categories - [Engineering](https://www.databend.com/blog-llms/engineering.txt): 43 posts. Engineering deep-dives: storage, execution engine, query optimization, Rust, and performance. - [Product](https://www.databend.com/blog-llms/product.txt): 79 posts. Product features, how-to guides, best practices, and release announcements. - [Customer Stories](https://www.databend.com/blog-llms/customer.txt): 6 posts. Customer stories: real-world migrations, cost savings, and production architectures. - [Weekly](https://www.databend.com/blog-llms/weekly.txt): 78 posts. Databend weekly digests: new features, bug fixes, and community updates. ## Latest Posts - [Same PASS, Different Paths: Evaluating Agent Traces at the Span Level with Jev](https://www.databend.com/blog/category-engineering/same-pass-different-paths-span-level-eval): 2026-09-24 — Two coding agents can run the same test suite and produce byte-for-byte identical patches, yet take very different routes. - [What PASS Misses: Lessons from Six Coding Agent Traces](https://www.databend.com/blog/category-engineering/what-pass-misses-six-coding-agent-traces): 2026-09-24 — I asked gpt-6-astra and claude-fable-5.1 to fix the same serde_json parsing bug through Evot, Pi, and DeepSeek Harness. All six runs passed the full test suite, and their core patches were byte-for-byte identical. - [Jev Went Viral, So We Integrated It into Lakehouse](https://www.databend.com/blog/category-engineering/jev-in-lakehouse): 2026-09-22 — Can classification and filtering based on semantic meaning stay inside SQL? This article walks through a Python UDF integration between Databend and Jev, then examines where remote model calls fit in terms of latency, cost, and data governance. - [Native data lineage in Databend: Tracing metric sources and assessing change impact](https://www.databend.com/blog/category-engineering/databend-native-data-lineage): 2026-09-17 — Available since Databend v1.2.935, Databend's native Data Lineage feature uses query plans to capture object-level and column-level dependencies directly inside the database. SQL queries and the Databend Cloud lineage graph provide two ways to inspect these relationships for change impact analysis, troubleshooting, data asset governance, and sensitive data tracking. - [Databend Incremental Materialized Views - Change Tracking, Incremental Refresh, and Consistent Reads](https://www.databend.com/blog/category-engineering/incremental-materialized-views): 2026-09-02 — Materialized views are more than persisted query results. This article explains how Databend combines Change Tracking, checkpoints, aggregate states, and read fix to reduce refresh cost without sacrificing query correctness. It also outlines where incremental materialized views work well today. - [Data Engineering in the AI Era - Taming Fragmented Agent Trace Pipelines with a Unified Lakehouse](https://www.databend.com/blog/category-engineering/unified-lakehouse-agent-traces): 2026-08-28 — AI accelerates application development, but it often leaves data teams with longer pipelines, more components, and inconsistent definitions across clouds. A unified, object-storage-based data platform can simplify this architecture by preserving raw data, processing changes incrementally, isolating workloads, and producing governed datasets for evaluation, analytics, and training. - [Databend Cluster Key Best Practices: Choosing Columns, Key Order, and Granularity](https://www.databend.com/blog/category-engineering/cluster-key-best-practices): 2026-08-25 — Do not copy every frequently filtered column into `CLUSTER BY`. Start with the query families responsible for the most cumulative scanning, identify the selective predicates they share, match time granularity to the typical query window, and test alternative key orders under controlled conditions. Validate both the physical layout with `clustering_information` and the actual scan reduction with `EXPLAIN ANALYZE`, then compare the long-term query savings with the cost of maintaining that layout. - [From Traditional Partitions to Micro-Partitions—How Snowflake and Databend Reduce Data Scanning](https://www.databend.com/blog/category-engineering/cluster-key-pruning): 2026-08-20 — 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. - [From Kafka to Databend Cloud: Engineering a Trillion-Scale Agent Trace Ingestion Pipeline](https://www.databend.com/blog/category-engineering/agent-trace-ingestion): 2026-08-12 — 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 Million-Token Agents to Eval-Ready Data](https://www.databend.com/blog/category-customer/agent-trace-eval-pipeline): 2026-08-06 — From Kafka ingestion and schema-flexible raw storage to incremental processing, Evals, and governed analytics for million-token AI workloads. ## More - [Blog](https://www.databend.com/blog/): Blog home page. - [llms.txt](https://www.databend.com/llms.txt): Site llms.txt: index of Databend products, solutions, and resources.