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Open Source
Tool Comparison
ClickHouse
Real-time OLAP database — sub-second queries on billions of rows, open-source.
VS
At a Glance
| Attribute | ClickHouse | DuckDB |
|---|---|---|
| License / Pricing | Open Source | Open Source |
| Type | data | data |
| GitHub Stars | — | — |
| Rating | 4.6/5 | 4.8/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 5 listed | 5 listed |
| Categories | Data Warehouse | Data Warehouse |
Key Features
ClickHouse
- Columnar storage with LZ4/ZSTD compression for 10x storage efficiency
- Vectorized query execution for extreme analytical throughput
- MergeTree table engine family with partitioning and TTL
- Materialized Views for pre-aggregating high-cardinality streams
- Native Kafka and S3 integrations for real-time data ingestion
- ClickHouse Cloud with auto-scaling and managed backups
DuckDB
- In-process OLAP — no server, no setup, runs inside your Python process
- Columnar vectorized execution for fast analytical queries
- Direct querying of Parquet, CSV, JSON, and Arrow without loading
- Full SQL support including window functions, CTEs, and PIVOT
- Extensions for spatial data (duckdb_spatial), Postgres scanner, and Arrow
- MotherDuck for managed DuckDB cloud with shared data
Real-World Use Cases
ClickHouse
Real-time product analytics backend
Stream click and event data from Kafka into ClickHouse via Kafka table engine
Log analytics at scale
Ingest application logs from Filebeat or Vector into ClickHouse
DuckDB
Analyze a large Parquet dataset without Spark
pip install duckdb
Lightweight local data warehouse with dbt
Install dbt-duckdb adapter and configure a DuckDB profile
Integrations
ClickHouse
apache-kafkaairbytegrafanasupersetdbt
DuckDB
dbtsqlmeshdagsterprefectapache-arrow
🏆 Which should you choose?
Choose ClickHouse if…
- → you're already in the Data Warehouse ecosystem and prefer ClickHouse's workflow
Choose DuckDB if…
- → you're already in the Data Warehouse ecosystem and prefer DuckDB's workflow