How does this skill improve ClickHouse query performance?

It provides specific patterns for index-first filtering, specialized aggregation functions like uniq and quantile, and guidance on avoiding costly operations like JOINs and SELECT *.

Does it include examples for advanced analytics?

Yes, the skill provides templates for time-series analysis, retention/cohort analysis, and conversion funnel calculations optimized for ClickHouse.

Can this help with database schema design?

Absolutely. It covers various MergeTree engine types, partitioning strategies, and sorting key selection to ensure maximum compression and query speed.

Does it support real-time data ingestion patterns?

Yes, it includes best practices for batching inserts in TypeScript/Node.js and using Materialized Views for real-time data aggregation.

ClickHouse Analytics & Data Engineering

Name: ClickHouse Analytics & Data Engineering
Author: LuciferDono

byLuciferDono

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데이터베이스 관리

Optimizes high-performance analytical workloads with ClickHouse-specific table designs, query patterns, and data engineering best practices.

This skill equips Claude with deep expertise in ClickHouse, a high-performance columnar OLAP database. It provides implementation patterns for various MergeTree engines, real-time data aggregation strategies using Materialized Views, and optimized query structures for large-scale datasets. Developers can leverage this skill to build efficient data pipelines, perform complex time-series and cohort analysis, and ensure optimal database performance through refined indexing and batch insertion techniques. It is essential for teams building data-intensive applications where low-latency analytics are a core requirement.

주요 기능

011 GitHub stars

02Real-time aggregation via Materialized Views and stateful functions

03Optimized analytical query patterns for time-series and funnel analysis

04Advanced MergeTree engine patterns (Replacing, Aggregating, and Standard)

05Performance monitoring and system table diagnostic queries

06High-efficiency batch and streaming data insertion strategies

사용 사례

01Implementing scalable ETL pipelines from PostgreSQL or external streams to ClickHouse

02Designing high-concurrency analytics dashboards for multi-billion row datasets

03Refactoring slow analytical SQL queries to utilize ClickHouse columnar optimizations

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add luciferdono/labs clickhouse-io

For use in Claude.ai and ChatGPT