Can I use this for complex user behavior analysis?

Absolutely. It contains specific SQL patterns for funnel analysis, retention tracking, and monthly cohort calculations optimized for column-oriented storage.

What primary database engines does this skill cover?

It focuses on the MergeTree family, including ReplacingMergeTree for deduplication and AggregatingMergeTree for pre-calculated metrics.

How does it improve query performance?

The skill provides specific guidance on sorting keys, partition strategies, and using ClickHouse-specific aggregate functions like quantiles and uniqMerge.

Does it support data ingestion patterns?

Yes, it includes best practices for bulk inserts in Node.js/TypeScript and streaming ingestion to prevent performance bottlenecks.

ClickHouse Analytics Expert

Name: ClickHouse Analytics Expert
Author: Infopibe

byInfopibe

•

データベース管理

Provides specialized patterns and best practices for ClickHouse database design, high-performance query optimization, and real-time data engineering.

This skill equips developers with deep expertise in ClickHouse for high-performance online analytical processing (OLAP). It covers advanced table engine configurations like MergeTree and AggregatingMergeTree, efficient data ingestion strategies using batch and stream patterns, and sophisticated query optimizations for sub-second analytics on massive datasets. Whether you are building real-time dashboards, performing time-series analysis, or setting up complex ETL pipelines, this skill provides the architectural guidance and implementation patterns needed for production-grade analytical workloads.

主な機能

011 GitHub stars

02Real-time aggregation techniques using Materialized Views

03Efficient data ingestion patterns for both batch and streaming workflows

04Advanced table engine implementation (MergeTree, ReplacingMergeTree, AggregatingMergeTree)

05Pre-built analytical patterns for time-series, funnel, and cohort analysis

06High-performance SQL query optimization and indexing strategies

ユースケース

01Implementing efficient ETL/CDC pipelines from relational databases to ClickHouse

02Optimizing slow analytical queries on multi-billion row datasets

03Designing scalable data warehouse schemas for real-time monitoring

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add infopibe/everything-claude-code clickhouse-io

For use in Claude.ai and ChatGPT

主な機能

011 GitHub stars

02Real-time aggregation techniques using Materialized Views

03Efficient data ingestion patterns for both batch and streaming workflows

04Advanced table engine implementation (MergeTree, ReplacingMergeTree, AggregatingMergeTree)

05Pre-built analytical patterns for time-series, funnel, and cohort analysis

06High-performance SQL query optimization and indexing strategies

ユースケース

01Implementing efficient ETL/CDC pipelines from relational databases to ClickHouse

02Optimizing slow analytical queries on multi-billion row datasets

03Designing scalable data warehouse schemas for real-time monitoring

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add infopibe/everything-claude-code clickhouse-io

For use in Claude.ai and ChatGPT