Can this skill help with real-time data aggregation?

Yes, it includes detailed patterns for using AggregatingMergeTree and Materialized Views to maintain real-time aggregate statistics efficiently.

What is the primary focus of this ClickHouse skill?

It focuses on high-performance analytical patterns, data engineering best practices, and query optimization specifically tailored for ClickHouse database architectures.

Does it cover data ingestion best practices?

Absolutely. It provides best practices for batch and streaming insertions to ensure maximum throughput and avoid common performance bottlenecks.

Is it suitable for time-series analysis?

Yes, it provides specialized query patterns for cohort analysis, conversion funnels, and time-series metrics like daily active users.

Does it include performance monitoring scripts?

Yes, the skill contains patterns for auditing system query logs and monitoring disk usage and merge operations.

ClickHouse Analytics Patterns

Name: ClickHouse Analytics Patterns
Author: Fabio29T

byFabio29T

0•

Database Management

Provides high-performance analytical patterns and data engineering best practices for ClickHouse database optimization.

This skill equips Claude with specialized knowledge for architecting and optimizing ClickHouse databases, a leading OLAP system. It covers essential patterns such as MergeTree engine configurations, deduplication strategies using ReplacingMergeTree, and real-time aggregation via Materialized Views. Whether you are building complex data pipelines, performing time-series analysis, or optimizing slow queries, this skill provides domain-specific guidance and SQL implementation patterns needed to handle massive datasets with high efficiency.

Key Features

01Efficient batch and streaming data insertion strategies

02Specialized table engine design patterns (MergeTree, AggregatingMergeTree)

03Real-time analytical patterns including funnel and cohort analysis

04Performance monitoring and system health check queries

050 GitHub stars

06Query optimization techniques for large-scale OLAP workloads

Use Cases

01Optimizing slow analytical queries using materialized views and indexing

02Building robust ETL pipelines for streaming data into ClickHouse

03Designing high-throughput time-series databases for market analytics

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add fabio29t/everything-claude clickhouse-io

For use in Claude.ai and ChatGPT

Key Features

01Efficient batch and streaming data insertion strategies

02Specialized table engine design patterns (MergeTree, AggregatingMergeTree)

03Real-time analytical patterns including funnel and cohort analysis

04Performance monitoring and system health check queries

050 GitHub stars

06Query optimization techniques for large-scale OLAP workloads

Use Cases

01Optimizing slow analytical queries using materialized views and indexing

02Building robust ETL pipelines for streaming data into ClickHouse

03Designing high-throughput time-series databases for market analytics

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add fabio29t/everything-claude clickhouse-io

For use in Claude.ai and ChatGPT