ClickHouse vs Snowflake

For real-time analytics

ClickHouse is designed for real-time data analytics and exploration at scale. Snowflake is a cloud data warehouse that is well-optimized for executing long-running reports and ad-hoc data analysis. When it comes to real-time analytics, ClickHouse shines with faster queries at a fraction of the cost.

Discover these insights and more in our benchmark study that compares ClickHouse with Snowflake for real-time analytics. Learn how to escape from Snowflake's climbing costs and revamp your data strategy below.

ClickHouse performance compared to Snowflake for real-time analytics

2x

faster queries

38%

better compression

3-5x

reduction in costs

"With Snowflake, we were using the standard plan, small compute, which cost nearly six times more than ClickHouse Cloud. We got several seconds query time and no materialized views. With ClickHouse Cloud's production instance, we are getting sub-second query time along with materialized views. The decision to switch was a no-brainer for us.”

“Snowflake [was] too slow and costly for our needs. While it performs well for processing in-house data, it becomes quite expensive when handling real-time customer data within a product, which negatively impacts the product's unit economics.”

Executive summary

Overview

Our benchmark analysis demonstrates that ClickHouse Cloud outperforms Snowflake across the critical dimensions for real-time analytics: query latency and cost.

Objective

Reports from customers have indicated that migrating real-time analytics workloads from Snowflake to ClickHouse Cloud has not only increased query performance but also reduced expenses for their businesses. Thus, the objective of our benchmark analysis is to deeply understand and outline the differences and similarities between ClickHouse Cloud and Snowflake for real-time analytics. We compare the performance and cost of both systems.

Approach

We benchmark, in ClickHouse Cloud and Snowflake, a set of real-time analytics queries that are representative of many real-time data applications. The cost is recorded for running each benchmark test, considering data loading and storage. Finally, this expense analysis is projected and compared for a production environment and workload.

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