ClickHouse vs Snowflake

Compare and contrast ClickHouse and Snowflake by architecture, ingestion, queries, performance, and scalability.

Compare ClickHouse to Rockset here

Compare Snowflake to Rockset here

ClickHouse vs Snowflake Architecture

Architecture

ClickHouse

Snowflake

Deployment model

• Self-managed on-premises or on cloud infrastructure • Several managed cloud services available

SaaS - infrastructure, software and cluster ops managed by service provider

Use of storage hierarchy

• Designed to use hard disk drives for storage • Can also use SSD if available

Cloud object storage for shared data accessible from any virtual warehouse

Isolation of ingest and query

Yes - separate virtual warehouses for batch data loading, ELT jobs and queries

Separation of compute and storage

No - although ClickHouse Cloud decouples compute and cloud storage

Yes

Isolation for multiple applications

Yes - separate virtual warehouses for each workload

ClickHouse is open source and can be deployed anywhere. Several vendors such as ClickHouse, the company, and Tinybird also offer cloud versions. Compute and storage are tightly coupled, although ClickHouse Cloud was rearchitected to decouple compute and storage. ClickHouse Cloud pricing is based on compute and storage usage.

Snowflake is the data warehouse built for the cloud. Snowflake is well-known for separating storage and compute for better price performance. With Snowflake, multiple virtual warehouses can be spun up or down for batch data loading, transformations and queries all on the same shared data.

ClickHouse vs Snowflake Ingestion

Ingestion

ClickHouse

Snowflake

Data sources

• Core integrations for ingestion from Kafka, S3, Google Cloud Storage • Other partner and community integrations available

• Third party ETL tool to ingest data into Snowflake including Fivetran, Hevo or Striim • Bulk loading from S3, GCS, Azure Blob Storage • Sink Connector for Apache Kafka in Confluent Cloud

Semi structured data

• JSON Object type for handling nested JSON • Automatically infers schema from a subset of rows

Ingests JSON and XML as a VARIANT data type

Transformations and rollups

Yes - several storage engines can pre-aggregate data

• Third party ELT/ETL tools like dbt • Simple COPY commands at data loading for column recording, omission and casts

ClickHouse has core integrations from common sources such as Kafka and S3. It recently introduced greater ability to handle semi-structured data using the JSON Object type and automatic schema inference.

Snowflake is an immutable data warehouse that is built for batch ingestion and relies heavily on the modern data stack ecosystem for data connectors and transformations. Snowflake has a number of integrations to ETL and ELT solutions including Fivetran, Hevo, Striim and dbt. While Snowflake does have support for semi-structured data in the form of a VARIANT type, it is best to structure the data for optimal query performance.

ClickHouse vs Snowflake Performance

Performance

ClickHouse

Snowflake

Updates

• Writes to immutable files • Updates rewrite and merge data files asynchronously • Frequent updates are not recommended due to potential for large rewrites

Data warehouse with immutable storage. Updates rewrite and merge entire partitions

Indexing

• Primary indexes use sparse indexing on data ordered by primary key • Secondary data skipping indexes

Query latency

Sub-100ms to seconds, optimized for large-scale aggregations

Seconds to minutes on petabytes of data

Storage format

• Column-oriented • Heavily compressed to minimize storage footprint

Compressed columnar format stored in cloud object storage

Streaming ingest

Recommends inserting in batches of >1000 rows and <1 insert per second

• Ingests on a batch basis • Snowpipe typically ingests in minutes

ClickHouse leverages column orientation and heavy compression for better performance on analytics workloads. It also uses indexing to accelerate queries as well. While ClickHouse use cases often involve streaming data from Kafka, batching data is recommended for efficient ingestion.

Snowflake is designed for batch analytics with analysts and data scientists infrequently accessing large-scale data for trend analysis. Snowflake, like many data warehouses, is immutable and does not support frequently changing data efficiently. Snowflake uses a columnar store to return aggregations and metrics efficiently, often with query response times in the seconds to minutes on petabytes of data.

ClickHouse vs Snowflake Queries

Queries

ClickHouse

Snowflake

Joins

Yes

Query language

SQL

Developer tooling

• API for querying SQL via POST command • Python, Java, Node.js and Go language clients

• SQL APIs - make SQL calls to Snowflake programmatically • UDFs for Javascript, Python, Java and SQL functions • Go, JDBC, .NET, Node.js, ODBC, PHP, Python drivers

Visualization tools

Integrations with Metabase, Superset, Grafana, Tableau, Deepnote and Rocket BI

Integrations with QuickSight, Chartio, Domo, Looker, PowerBI, Mode, Qlik, Sigma, Sisense, Tableau, ThoughtSpot and more

ClickHouse uses SQL for querying, with support for SQL joins. ClickHouse integrates with some common tools for visual analytics, including Superset, Grafana and Tableau.

Snowflake supports SQL as its native query language and can perform SQL joins. Snowflake for developers introduced a number of developer tools including SQL APIs, UDFs and drivers to support application development. As Snowflake was originally built for business intelligence workloads, it integrates with a number of visualization tools for trend analysis.

ClickHouse vs Snowflake Scalability

Scalability

ClickHouse

Snowflake

Vertical scaling

Scale up single-node ClickHouse for vertical scaling

Resize virtual warehouses via web interface or using DDL commands for warehouses

Horizontal scaling

• Compute and storage scaled in lockstep • Data rebalanced to populate newly added nodes • Cloud offerings automate some of the scaling and rebalancing effort

• Multi-cluster warehouses allocate additional clusters for higher concurrency workloads • Auto scaling policies can be set

ClickHouse can be used in both single-node and distributed modes. Tight coupling of compute and storage and the need to rebalance data make scaling out more complex, but cloud versions of ClickHouse help automate this process.

Snowflake virtual warehouses can be scaled up for faster queries or scaled out using multi-cluster warehouses to support higher concurrency workloads. Snowflake has shared blob storage that scales automatically and independently.