Does this skill help with data ingestion?

Yes, it includes detailed patterns for both bulk and streaming inserts, helping you avoid common performance pitfalls like sending too many small insert requests.

How does this skill assist with schema design?

It provides templates for various MergeTree engines, partitioning strategies based on time, and advice on selecting the optimal ordering keys for compression and speed.

Can it help optimize slow analytical queries?

Absolutely. It provides guidance on efficient filtering, ClickHouse-specific aggregation functions, and diagnostic queries to identify and resolve performance bottlenecks.

What is the ClickHouse-io skill for Claude Code?

It is a specialized capability that provides Claude with patterns and best practices for developing high-performance analytical workloads and data engineering pipelines on ClickHouse.

Does it cover advanced features like Materialized Views?

Yes, the skill includes implementation patterns for Materialized Views to enable real-time data aggregation and efficient metric rollups.

ClickHouse Analytics & Data Patterns

Name: ClickHouse Analytics & Data Patterns
Author: WorldFlowAI

byWorldFlowAI

Gestión de Bases de Datos

Optimizes ClickHouse database performance with expert patterns for schema design, high-volume data ingestion, and complex analytical queries.

Acerca de

This skill empowers Claude to architect and maintain high-performance ClickHouse environments for Online Analytical Processing (OLAP). It provides comprehensive guidance on MergeTree engine variations, materialized views for real-time aggregation, and optimized SQL syntax specifically tailored for ClickHouse's column-oriented architecture. Whether you are building real-time dashboards, conducting cohort analysis, or designing efficient ETL pipelines, this skill ensures best practices for data types, partitioning, and query optimization are followed to minimize latency and maximize storage compression.

Características Principales

Real-time materialized view configurations for automated rollups
0 GitHub stars
Optimized SQL syntax for analytical functions and window operations
Advanced MergeTree engine patterns for deduplication and aggregation
Diagnostic queries for monitoring table statistics and slow query logs
High-performance bulk and streaming data ingestion strategies

Casos de Uso

Designing a scalable schema for real-time market data analytics
Building automated ETL pipelines to sync relational data to ClickHouse
Optimizing slow-running queries through proper indexing and partitioning

Acerca de

Características Principales

Real-time materialized view configurations for automated rollups
0 GitHub stars
Optimized SQL syntax for analytical functions and window operations
Advanced MergeTree engine patterns for deduplication and aggregation
Diagnostic queries for monitoring table statistics and slow query logs
High-performance bulk and streaming data ingestion strategies

Casos de Uso

Designing a scalable schema for real-time market data analytics
Building automated ETL pipelines to sync relational data to ClickHouse
Optimizing slow-running queries through proper indexing and partitioning