ClickHouse migrations and analytics checks in Buddy pipelines
Buddy now has a native ClickHouse target. Store the connection once, then run queries, schema migrations, and post-deployment data checks from any pipeline.
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Buddy now has a native ClickHouse target. Store the connection once, then run queries, schema migrations, and post-deployment data checks from any pipeline.
We ran 92 search and analytics queries over 100M and 1B OpenTelemetry logs against vanilla Postgres, ParadeDB with pg_search, TigerData with pg_textsearch and SereneDB.
Your application code branches in seconds. Your database still gets copied, shared, and fought over. Here is how database branching closes that gap, where it breaks down, and what has to sit around it before it makes releases safer.
Issue #3 of The State of Serene: sloppy phrase search, Lucene query syntax, REINDEX and search over Iceberg, per-node score and merge control, Azure blob storage and a faster postings read path.
Build a SereneDB full-text index over tables in Postgres or ClickHouse, choose which columns stay local and query the results through SQL.
DynamoDB now supports native vector search with single-digit millisecond latency at 99%+ recall. It is designed for any scale, even trillions of vectors and requires zero infrastructure management.
Issue #2 of The State of Serene: term-dictionary facets, IVF vector search, online CREATE INDEX, PostgreSQL RBAC, a ClickHouse connector and the SearchBench numbers against ArangoDB.
One year after we started SereneDB we ran 92 search and analytics queries over 1B OpenTelemetry logs and put the numbers next to ArangoDB to see how far we have come.
Database | Data Engineering | Data Modeling | SQL What you will learn? This video will talk about the major breakthrough happen in the data domain and it's with Databases.
Issue #1 of The State of Serene: what SereneDB is — lakehouse search and analytics for your agents — what it does today, and everything we shipped in June 2026.
Building a code-search system over 14 GB of programming solutions in a data lake — searching Parquet in place, without moving the data.
Why SQL is the right language for Search-OLAP: BM25 scores and vector distances belong in the same query plan as your joins and aggregations.
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