Used For

• +**Your app monitoring queries timeout after 30 minutes on a billion log entries** → Get answers in under 50 milliseconds with columnar storage that skips irrelevant data
• +**Analysts wait 8 hours for batch reports to finish running** → Self-serve SQL queries return in seconds, no more overnight processing
• +**Your Elasticsearch cluster costs $5k/month and still feels slow** → ClickHouse handles the same log volume 10x faster for half the infrastructure cost
• +Real-time data ingestion without locks - millions of rows per second while analysts query simultaneously
• +Handles time-series data brilliantly with automatic partitioning and data skipping

Best For

• >Your log analysis takes hours and breaks pipelines at 3am
• >Hit that wall where PostgreSQL chokes on billion-row reports
• >Engineering team exists and your data problems are worth the complexity

Not For

• -Teams under 50 people without dedicated data engineers — you'll waste weeks on setup instead of just using BigQuery
• -Anyone needing fast lookups of individual records — it's built for big scans, not finding one customer's order
• -Companies wanting plug-and-play analytics — this requires Linux skills, schema design, and ongoing maintenance

Pairs With

• *Kafka (streams your app logs and events into ClickHouse in real-time)
• *dbt (transforms raw data before it hits ClickHouse so your queries stay fast)
• *Grafana (where you build dashboards because ClickHouse's UI is purely functional)
• *S3 (cheap storage for older data that you query less often)
• *Airflow (orchestrates your data pipelines and handles the ETL scheduling)
• *Kubernetes (if you're running the self-hosted version and need it to not fall over)

The Catch

• !Self-hosting means you're now running database infrastructure — expect $2k+/month just for the SSDs and RAM it demands
• !Point queries (like "show me user 12345's data") are surprisingly slow because it's optimized for scanning millions of rows, not finding one
• !Cloud version pricing can shock you during busy periods — burst queries rack up compute charges fast

Bottom Line

Turns terabytes of data into sub-second queries, but you'll need someone who speaks Linux and SQL.