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ClickHouse vs Snowflake

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

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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
No
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
No
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.

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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
No
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
Yes
Query language
SQL
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.

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