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Comparisons

ClickHouse vs BigQuery

ClickHouse vs BigQuery
BigQuery handles ad-hoc queries and smaller data volumes effectively, but scaling turns cost and performance management into a significant challenge. Read more below to learn about how ClickHouse and BigQuery compare in cost, performance, and with supported features.
Read our comprehensive guide about migrating from ClickHouse to BigQuery

ClickHouse

  • 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.

BigQuery

  • 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.

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Why developers choose ClickHouse

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BigQuery’s query latency

Achieving sub-second query response times and supporting highly concurrent workloads can be painful in BigQuery, if not impossible.
ClickHouse is purpose-built for real-time, large-volume, data analytics. It’s the fastest and most resource-efficient database for analytics and is designed to serve queries with high concurrency without enforcing limits on the number of parallel queries.
Whether you’re aggregating large volumes of data in real-time, interactively slicing and dicing on the fly, or powering customer-facing dashboards, ClickHouse ensures blazing speed.
Latency when querying 1 billion rows
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We needed a solution that could scale, but also provide end-user facing analytics capabilities with low latency and high throughput. Read blog
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BigQuery’s high cost

BigQuery’s pricing model can lead companies to artificially constrain usage or access to data, leading to lower ROI.
ClickHouse is designed to manage huge volumes of data efficiently. Its efficient management of resources helps to maximize its cost-effectiveness. ClickHouse was designed from the ground up for best-in-class resource utilization.
For example, Prefect reduced costs by 33% moving from BigQuery to ClickHouse, and Juspay reduced its operating expenses by 10x after migrating its analytics workloads from BigQuery to ClickHouse.
Cost for querying 1 billion rows
Quote
[BigQuery] discourages data usage. Instead of encouraging analysts to query the database in any and all ways they can imagine you’ll end up worrying about needing to limit them and come up with processes for controlling the volume of data being used.
We simply don’t want the hassle of trying to figure out in advance of how many BigQuery slots to purchase - what a headache! Read blog
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When not to migrate from BigQuery to ClickHouse Cloud yet?

If you need multi-statement transactions or extensive joins over highly normalized tables.
Both are on our roadmap for 2025.

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