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chdb-datastore

Replace pandas operations with ClickHouse-backed execution for large datasets.

1.6k|107|Updated Feb 15, 2026
Search Tags:#data analysis#pandas#etl#clickhouse#cross-source joins#data lakes#sql acceleration
One-click install
npx skills add https://github.com/MapleTechLabs/maple --skill chdb-datastore-mapletechlabs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chdb-datastore
Source: https://github.com/MapleTechLabs/maple/tree/main/.agents/skills/chdb-datastore
Command: npx skills add https://github.com/MapleTechLabs/maple --skill chdb-datastore-mapletechlabs

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires chdb, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill eliminates the performance bottlenecks of standard pandas on large datasets and removes the complexity of joining data across disparate sources like relational databases, cloud storage, and data lakes.

Core Features & Use Cases

  • Pandas-compatible API: Use identical pandas syntax with only a single import line change, no code rewrites required.
  • Cross-source joins: Seamlessly combine data from 16+ sources including MySQL, PostgreSQL, S3, Parquet, Iceberg, and Delta Lake in a single query.
  • Use case: An analyst can join customer records from MySQL with order logs from S3 Parquet files to calculate regional revenue metrics in seconds, avoiding manual ETL pipelines.

Quick Start

Use the chdb-datastore skill to analyze the attached sales.parquet file by filtering for orders over $1000, grouping by product category, and summing total revenue.

Frequently Asked Questions about chdb-datastore

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I speed up slow pandas operations on large datasets without rewriting my code?▼

To speed up slow pandas operations on large datasets, you can use a ClickHouse-backed drop-in replacement that executes identical pandas syntax 10-100x faster with only a single import change and no code modifications.

Can I join data across MySQL, S3 Parquet, and data lakes in a single query?▼

You can join data across MySQL, S3 Parquet, and data lakes in a single query by using cross-source joins that seamlessly combine records from 16+ disparate sources without manual ETL pipelines.

What is the best way to accelerate SQL queries on data lakes and cloud object storage?▼

The best way to accelerate SQL queries on data lakes and cloud object storage is using a ClickHouse-backed execution engine that processes large datasets directly across 16+ sources for rapid analysis.

Do I need to install ClickHouse separately to use a pandas-compatible API for data analysis?▼

You do not need to install ClickHouse separately to use this pandas-compatible API, as the required execution engine is bundled entirely within the local dependency for immediate data analysis.

Does this pandas replacement support cross-source joins with Iceberg and Delta Lake formats?▼

This pandas replacement supports cross-source joins with Iceberg and Delta Lake formats, allowing you to combine relational databases, cloud object storage, and data lakes seamlessly in one query.

Why does pandas run out of memory when analyzing large Parquet files and how do I avoid it?▼

Pandas runs out of memory on large Parquet files due to in-memory execution limits, but you can avoid this by switching to a ClickHouse-backed replacement that processes large datasets 10-100x faster.

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