See Rockset
in action

Get a product tour with a Rockset engineer

Snowflake vs ClickHouse

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

Compare Snowflake to Rockset here

Compare ClickHouse to Rockset here

Snowflake Architecture vs ClickHouse

Separation of compute and storage
No - although ClickHouse Cloud decouples compute and cloud storage
Isolation of ingest and query
Yes - separate virtual warehouses for batch data loading, ELT jobs and queries
Isolation for multiple applications
Yes - separate virtual warehouses for each workload
Use of storage hierarchy
Cloud object storage for shared data accessible from any virtual warehouse
• Designed to use hard disk drives for storage • Can also use SSD if available
Deployment model
SaaS - infrastructure, software and cluster ops managed by service provider
• Self-managed on-premises or on cloud infrastructure • Several managed cloud services available

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 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 Ingestion vs ClickHouse

Data sources
• 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
• Core integrations for ingestion from Kafka, S3, Google Cloud Storage • Other partner and community integrations available
Semi structured data
Ingests JSON and XML as a VARIANT data type
• JSON Object type for handling nested JSON • Automatically infers schema from a subset of rows
Transformations and rollups
• Third party ELT/ETL tools like dbt • Simple COPY commands at data loading for column recording, omission and casts
Yes - several storage engines can pre-aggregate data

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 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.

See Rockset in action
Get a product tour with a Rockset engineer.

Snowflake Queries vs ClickHouse

Query language
Developer tooling
• 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
• API for querying SQL via POST command • Python, Java, Node.js and Go language clients
Visualization tools
Integrations with QuickSight, Chartio, Domo, Looker, PowerBI, Mode, Qlik, Sigma, Sisense, Tableau, ThoughtSpot and more
Integrations with Metabase, Superset, Grafana, Tableau, Deepnote and Rocket BI

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

Snowflake Performance vs ClickHouse

Streaming ingest
• Ingests on a batch basis • Snowpipe typically ingests in minutes
Recommends inserting in batches of >1000 rows and <1 insert per second
Data warehouse with immutable storage. Updates rewrite and merge entire partitions
• Writes to immutable files • Updates rewrite and merge data files asynchronously • Frequent updates are not recommended due to potential for large rewrites
Storage format
Compressed columnar format stored in cloud object storage
• Column-oriented • Heavily compressed to minimize storage footprint
• Primary indexes use sparse indexing on data ordered by primary key • Secondary data skipping indexes
Query latency
Seconds to minutes on petabytes of data
Sub-100ms to seconds, optimized for large-scale aggregations

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 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 Scalability vs ClickHouse

Vertical scaling
Resize virtual warehouses via web interface or using DDL commands for warehouses
Scale up single-node ClickHouse for vertical scaling
Horizontal scaling
• Multi-cluster warehouses allocate additional clusters for higher concurrency workloads • Auto scaling policies can be set
• Compute and storage scaled in lockstep • Data rebalanced to populate newly added nodes • Cloud offerings automate some of the scaling and rebalancing effort

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.

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.

See Rockset in action
Sub-second SQL on streaming data with surprising efficiency.