Snowflake vs ClickHouse

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

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Snowflake vs ClickHouse Architecture

Architecture

Snowflake

ClickHouse

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

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

Isolation of ingest and query

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

Separation of compute and storage

Yes

No - although ClickHouse Cloud decouples compute and cloud storage

Isolation for multiple applications

Yes - separate virtual warehouses for each workload

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 vs ClickHouse Ingestion

Ingestion

Snowflake

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.

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Snowflake vs ClickHouse Performance

Performance

Snowflake

ClickHouse

Updates

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

Indexing

• 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

Storage format

Compressed columnar format stored in cloud object storage

• Column-oriented • Heavily compressed to minimize storage footprint

Streaming ingest

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

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

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 vs ClickHouse Queries

Queries

Snowflake

ClickHouse

Joins

Yes

Query language

SQL

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 vs ClickHouse Scalability

Scalability

Snowflake

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.

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