{"slug":"duckdb-vs-clickhouse)","title":"DuckDB vs ClickHouse","url":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)","faqCount":5,"faqs":[{"question":"When should I choose DuckDB over ClickHouse?","answer":"Choose DuckDB if your dataset is under 1TB, you need zero operational overhead, you want a database that embeds directly in your application with no separate server infrastructure, or you're building data analysis tools for Python/R/JavaScript environments. DuckDB is ideal for data scientists, analysts working with local datasets, and teams prioritizing simplicity over distributed scale."},{"question":"Can DuckDB handle real-time data ingestion like ClickHouse?","answer":"DuckDB supports data ingestion but is optimized for analytical queries on static or batch-loaded datasets. ClickHouse excels at real-time event streaming with insert rates of 1M+ rows per second, native Kafka integration, and built-in data replication. For real-time analytics at scale (>100K events/second), ClickHouse is the better choice."},{"question":"What are the licensing implications of using ClickHouse commercially?","answer":"ClickHouse is licensed under AGPL v3, which requires that any modifications or derivative works using ClickHouse must publish their source code. Organizations can: (1) use ClickHouse unmodified under AGPL, (2) purchase a commercial license from ClickHouse Inc. to use proprietary modifications, or (3) use DuckDB's MIT license if source code privacy is critical. Many enterprises choose the commercial license to avoid AGPL obligations."},{"question":"How do query languages differ between DuckDB and ClickHouse?","answer":"DuckDB uses standard SQL with PostgreSQL extensions, making it nearly 100% compatible with PostgreSQL syntax and allowing easy migration. ClickHouse uses ClickHouse SQL, a custom dialect with extensions like specialized aggregation functions (quantiles, groupArrayInsertAt) and lacks some standard features (common table expressions with limited support). Teams familiar with PostgreSQL will find DuckDB's SQL more intuitive, while ClickHouse's SQL is more powerful for specific analytics use cases."},{"question":"Which database is better for machine learning preprocessing?","answer":"DuckDB integrates seamlessly with Python/R/Arrow ecosystems, making it superior for ML preprocessing pipelines. It has native support for DuckDB-Arrow format, Parquet files, and Python UDFs. ClickHouse requires additional ETL steps to export data for ML. For ML workflows, DuckDB is the clear choice due to its tight integration with data science tools and zero operational friction."}],"faqPageSchema":{"@context":"https://schema.org","@type":"FAQPage","@id":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)#faq","url":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)","inLanguage":"en-US","name":"DuckDB vs ClickHouse — FAQ","description":"Frequently asked questions about DuckDB vs ClickHouse","dateModified":"2026-07-09T15:31:23.186Z","author":{"@type":"Organization","@id":"https://www.aversusb.net/#organization","name":"A Versus B"},"publisher":{"@type":"Organization","@id":"https://www.aversusb.net/#organization","name":"A Versus B"},"isPartOf":{"@type":"Article","@id":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)#article"},"license":"https://creativecommons.org/licenses/by/4.0/","speakable":{"@type":"SpeakableSpecification","@id":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)#faq-speakable","cssSelector":[".faq-answer"]},"mainEntity":[{"@type":"Question","@id":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)#q1","name":"When should I choose DuckDB over ClickHouse?","answerCount":1,"acceptedAnswer":{"@type":"Answer","@id":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)#a1","text":"Choose DuckDB if your dataset is under 1TB, you need zero operational overhead, you want a database that embeds directly in your application with no separate server infrastructure, or you're building data analysis tools for Python/R/JavaScript environments. DuckDB is ideal for data scientists, analysts working with local datasets, and teams prioritizing simplicity over distributed scale.","inLanguage":"en-US","url":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)","upvoteCount":1,"author":{"@type":"Organization","@id":"https://www.aversusb.net/#organization","name":"A Versus B"}}},{"@type":"Question","@id":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)#q2","name":"Can DuckDB handle real-time data ingestion like ClickHouse?","answerCount":1,"acceptedAnswer":{"@type":"Answer","@id":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)#a2","text":"DuckDB supports data ingestion but is optimized for analytical queries on static or batch-loaded datasets. ClickHouse excels at real-time event streaming with insert rates of 1M+ rows per second, native Kafka integration, and built-in data replication. For real-time analytics at scale (>100K events/second), ClickHouse is the better choice.","inLanguage":"en-US","url":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)","upvoteCount":1,"author":{"@type":"Organization","@id":"https://www.aversusb.net/#organization","name":"A Versus B"}}},{"@type":"Question","@id":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)#q3","name":"What are the licensing implications of using ClickHouse commercially?","answerCount":1,"acceptedAnswer":{"@type":"Answer","@id":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)#a3","text":"ClickHouse is licensed under AGPL v3, which requires that any modifications or derivative works using ClickHouse must publish their source code. Organizations can: (1) use ClickHouse unmodified under AGPL, (2) purchase a commercial license from ClickHouse Inc. to use proprietary modifications, or (3) use DuckDB's MIT license if source code privacy is critical. Many enterprises choose the commercial license to avoid AGPL obligations.","inLanguage":"en-US","url":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)","upvoteCount":1,"author":{"@type":"Organization","@id":"https://www.aversusb.net/#organization","name":"A Versus B"}}},{"@type":"Question","@id":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)#q4","name":"How do query languages differ between DuckDB and ClickHouse?","answerCount":1,"acceptedAnswer":{"@type":"Answer","@id":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)#a4","text":"DuckDB uses standard SQL with PostgreSQL extensions, making it nearly 100% compatible with PostgreSQL syntax and allowing easy migration. ClickHouse uses ClickHouse SQL, a custom dialect with extensions like specialized aggregation functions (quantiles, groupArrayInsertAt) and lacks some standard features (common table expressions with limited support). Teams familiar with PostgreSQL will find DuckDB's SQL more intuitive, while ClickHouse's SQL is more powerful for specific analytics use cases.","inLanguage":"en-US","url":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)","upvoteCount":1,"author":{"@type":"Organization","@id":"https://www.aversusb.net/#organization","name":"A Versus B"}}},{"@type":"Question","@id":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)#q5","name":"Which database is better for machine learning preprocessing?","answerCount":1,"acceptedAnswer":{"@type":"Answer","@id":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)#a5","text":"DuckDB integrates seamlessly with Python/R/Arrow ecosystems, making it superior for ML preprocessing pipelines. It has native support for DuckDB-Arrow format, Parquet files, and Python UDFs. ClickHouse requires additional ETL steps to export data for ML. For ML workflows, DuckDB is the clear choice due to its tight integration with data science tools and zero operational friction.","inLanguage":"en-US","url":"https://www.aversusb.net/compare/duckdb-vs-clickhouse)","upvoteCount":1,"author":{"@type":"Organization","@id":"https://www.aversusb.net/#organization","name":"A Versus B"}}}]}}