A set of 28 validated rules for ClickHouse covering schema design, query optimization, and data ingestion.
chDB DataStore exposes the ClickHouse engine behind a pandas-compatible API so existing pandas code runs 10-100x faster on large datasets, with cross-source joins across MySQL, PostgreSQL, S3, MongoDB, ClickHouse, Iceberg, Delta Lake, and file formats like Parquet, CSV, JSON, Arrow, and ORC.
chDB SQL runs ClickHouse SQL queries directly against local files, remote databases, and cloud storage from Python without a running ClickHouse server.
Complements clickhouse-best-practices with decision frameworks and explicit provenance labels for designing ClickHouse architectures, selecting between ingestion or modeling patterns, and mapping best practices onto specific workloads.
Guides deployments to ClickHouse Cloud using clickhousectl, covering first-time production rollouts, managed service setup, and migration from a local ClickHouse to ClickHouse Cloud.
Covers the full flow from zero to a working local ClickHouse setup with clickhousectl: install ClickHouse, create a local server, create tables, and start developing an application against it.