Does it support Materialized Views?

Absolutely, it covers the creation and querying of Materialized Views to enable efficient, real-time pre-aggregation of incoming data streams.

How does this skill help with ClickHouse performance?

It provides specific indexing and sorting strategies, recommending optimal column types and engine settings to minimize query latency and maximize compression.

What complex analytical queries are supported?

The skill offers pre-built templates for sophisticated analysis such as user retention cohorts, conversion funnels, and advanced time-series aggregation.

Is it suitable for building data pipelines?

Yes, it provides ETL and Change Data Capture (CDC) code patterns for syncing data between transactional databases like PostgreSQL and ClickHouse.

Can it help with real-time data ingestion?

Yes, it includes standardized patterns for both batch and streaming inserts, helping you avoid common pitfalls like 'too many parts' errors caused by frequent small inserts.

ClickHouse Analytics Patterns

Name: ClickHouse Analytics Patterns
Author: unju-ai

byunju-ai

0•

データベース管理

Optimizes ClickHouse database schemas, query performance, and data engineering workflows for high-performance analytical workloads.

This skill provides specialized guidance for working with ClickHouse, the industry-leading column-oriented OLAP database. It assists developers in designing efficient table schemas using various MergeTree engines, implementing real-time data aggregation via materialized views, and optimizing complex analytical queries for speed and scale. By providing standardized patterns for batch insertion, time-series analysis, and performance monitoring, it ensures that Claude can help build scalable and performant data infrastructure capable of handling billions of rows with sub-second latency.

主な機能

01Query optimization techniques for high-speed filtering and ClickHouse-specific aggregations

02Advanced table design patterns using MergeTree, ReplacingMergeTree, and AggregatingMergeTree

030 GitHub stars

04Best practices for high-throughput batch and streaming data ingestion patterns

05Ready-to-use analytical templates for funnel analysis, retention, and cohort studies

06Implementation of real-time analytics and pre-aggregation using Materialized Views

ユースケース

01Building high-performance real-time dashboards and observability systems

02Designing robust ETL and CDC pipelines for syncing application data into an OLAP environment

03Migrating large-scale analytical workloads from traditional SQL databases to ClickHouse

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add unju-ai/ecc clickhouse-io

For use in Claude.ai and ChatGPT