Run pandas-compatible tabular workflows on a ClickHouse-backed DataStore.
npx skills add https://github.com/ClickHouse/agent-skills --skill chdb-datastore
Please help me install this Agent Skill. Skill: chdb-datastore Source: https://github.com/ClickHouse/agent-skills/tree/main/skills/chdb-datastore Command: npx skills add https://github.com/ClickHouse/agent-skills --skill chdb-datastore
An efficient DataStore for tabular data that accelerates pandas workloads by delegating operations to a ClickHouse-backed engine, reducing runtime and memory pressure.
Compose a DataStore from your tabular data sources and run a simple query to verify results.
High-intent search queries and answers about installing and using this skill.
You can speed up pandas data analysis by delegating tabular workflows to a ClickHouse-backed DataStore, which reduces runtime and memory pressure through lazy evaluation and optimized SQL execution.
Yes, cross-source joins allow you to seamlessly combine tabular data from local files, SQL databases like MySQL and PostgreSQL, and cloud sources like S3 within a single pandas-compatible query.
Lazy evaluation builds up operations sequentially and runs a single, efficient query only when results are needed, minimizing memory usage and accelerating complex aggregations and filtering.
Yes, the DataStore supports 16+ connectors including S3, MongoDB, Iceberg, Delta Lake, ClickHouse, MySQL, and PostgreSQL, enabling efficient inter-source queries across diverse environments.
Using a ClickHouse-backed DataStore with a pandas-compatible API and lazy evaluation minimizes memory pressure by pushing filtering, grouping, and aggregation operations to the optimized engine.
No, you write familiar pandas-like code that the engine automatically translates into optimized SQL, allowing you to accelerate dashboards and unify data without changing your workflow.
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