ClickHouse vs BigQuery
- Fast and efficient
Up to 95% faster querying speeds and 60% less storage space required.
- Cost-effective
Up to 100x more cost-effective.
- Modern SQL
Standard SQL enhanced with numerous extensions and improvements (e.g. lambda functions and higher-order functions), that make analytical tasks very user-friendly.
- Easy data analytics
150+ pre-built aggregation functions plus powerful aggregation combinators, fully vectorized and parallelized.
1300+ data processing functions for domains like mathematics, geo, machine learning, time series, and more.
- Rich data type support
Advanced data types like JSON, maps, and arrays plus over 80 array functions for modeling and solving a wide range of problems simply and intuitively.
- World class interoperability
Native support for reading data in over 90 file formats from most data sources which makes it easy to analyze data regardless of its shape and location.
- Fast and efficient
Slower querying speeds and requires more storage.
- Cost-effective
More costly for BigQuery for analytics workloads.
- Modern SQL
Support for only standard SQL can make analytics more complex.
- Easy data analytics
Requires writing more complex SQL due to its limited set of aggregate and regular data processing functions.
- Rich data type support
Support for limited number of data types including only 8 array functions.
- World class interoperability
Limited interoperability. Supports only 5 file formats and 19 data sources.
Why developers choose ClickHouse
BigQuery’s query latency
BigQuery’s high cost
When not to migrate from BigQuery to ClickHouse Cloud yet?
Both are on our roadmap for 2025.
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