Clickhouse Vs Snowflake: A Comprehensive Comparison

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October 1, 2024

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15 min read

Businesses, large or small, rely on data-driven insights to improve their strategic plans and operations performance. As the data grows in volume and complexity, the need to correctly process and analyze it becomes more important.

ClickHouse and Snowflake are two robust data analytics and management tools that help to process and analyze data with precision. While ClickHouse is a renowned real-time SQL database management system, Snowflake is known for its scalability and simplified data management.

This article will help you evaluate the key strengths and differences between ClickHouse vs Snowflake so that you can determine which technology best suits your analytical needs.

ClickHouse: Real-Time DBMS

ClickHouse is a column-oriented, high-performance SQL database management system optimized for online analytical processing (OLAP). The OLAP framework requires real-time response, and ClickHouse supports these frameworks through features like columnar storage, primary indexes, data compression, and more. It also offers deployment flexibility, allowing you to use the open-source version or opt cloud service where hosting and management are managed by the provider.

Key Features of ClickHouse

Column-Oriented: ClickHouse allows you to store data in columns instead of rows, where all the data for a single field is stored together. This storage format offers efficient data access.
Data Compression: The lesser data on the disk, the faster the query and inserts. ClickHouse uses codecs to make the data more compact. This reduces the storage space, minimizing the data reads to perform read or write operations.
Distributed Processing: Clickhouse enables distributed processing through shards. When you run a query, ClickHouse uses shard to work on the query at the same time. This helps to protect your data in case the server fails, or something goes wrong.

Snowflake: A Data Cloud

Snowflake is a robust data platform designed for cloud-native data storage, processing, and analytics. It is built upon a new SQL query engine with parallel processing capabilities that make it ideal for large-scale data operations.

As of the fourth fiscal quarter of 2024, Snowflake had 691 customers from the Forbes Global 2000. This suggests that Snowflake is the go-to data platform for large-scale and data-extensive operations. With features like metadata management and query parsing, Snowflake offers a comprehensive solution for your data needs.

Key Features of Snowflake

Standard and Extended SQL Support: Snowflake supports standard and advanced SQL to manage and query databases, schemas, and tables. It also allows you to implement scalar and tabular user-defined functions with the support of Java, Python, Scala, JavaScript, and SQL.
Virtual Warehouses: A virtual warehouse (VW) in Snowflake is a cluster of compute nodes. It provides you with resources such as memory and temporary storage. You can utilize these VWs to execute SQL queries that require compute storage and perform DML tasks, including loading, unloading and updating data during a Snowflake session.
Snowsight: Snowsight is a web interface that allows you to work with Snowflake data. The core functionality of Snowsight is to manage the Snowflake account, monitor activities, and help you query the data. You can utilize it to perform various tasks, including data analysis, monitor data loading, explore Snowflake objects, and more.

Key Differences ClickHouse vs Snowflake

Aspect	Snowflake	ClickHouse
Architecture	Decoupled storage and compute resources.	You can employ decoupled architecture within the ClickHouse Cloud option.
Query Performance	Enables fast querying through pruning, caching, and columnar storage.	Uses sparse indexing B-Tree, and other techniques for optimizing search operations.
Concurrency	The multi-cluster shared architecture allows you to work with other users simultaneously.	Optimized for high concurrency for OLAP workloads.
Compute Tuning	Facilitates tuning through the scalable virtual warehouse and result caching.	Facilitates tuning through data compression and utilizing CPU cache.
Pricing	Offers three pricing models: development, usage-based, and dedicated.	The pricing varies depending on the platform and region you choose to deploy.

Architecture

When comparing Clickhouse vs Snowflake architecture, these are the following points to consider:

Storage: Snowflake’s multi-cluster shared data architecture separates compute and storage resources and provides seamless scaling. On the other hand, you deploy Clickhouse with S3 to implement an architecture for separate storage.
Deployment: Snowflake has an innovative architecture that is natively designed for the cloud, and you can host it on AWS, Google Cloud, and Azure. Conversely, ClickHouse can be deployed on-premise and in the cloud.

Performance and Query

The following factors will help you evaluate the difference in performance and querying capabilities of ClickHouse vs Snowflake:

Indexes: Snowflake does not use indexing but a search optimization service to improve performance for lookup and analytical queries. Although, you can create secondary indexes for hybrid tables. On the contrary, Clickhouse uses techniques like sparse indexing and B-Tree to improve query performance.
Compute Tuning: You can achieve tuning in Snowflake through the virtual warehouse to distribute workloads to separate virtual instances. Also, Snowflake automatically caches data within these VWs, enhancing querying speed. ClickHouse, on the other hand, stores data in columns, optimizing query performance by utilizing CPU cache.

Scaling and Elasticity

The following aspects will help you understand the difference between the scaling capabilities of ClickHouse vs Snowflake:

Resource Scaling: Using auto-scaling functionality of Snowflake you can scale your warehouse up or down as per your querying needs. Alternatively, ClickHouse facilitates vertical and horizontal scaling, optimizing resource allocation.
Querying Concurrency: In Snowflake, the default number of concurrent queries that you can run within a warehouse is eight. On the other hand, within the ClickHouse Cloud service, there is no limit to the number of queries you can execute per second. However, you can only run 1000 concurrent queries per replica.

Pricing

Snowflake and ClickHouse both provide various pricing models that cater to the specific needs of your application. Let’s see how the cost varies for both:

Snowflake: Snowflake offers three main pricing models, including the Standard model, which provides only core functionalities. The Enterprise model for large-scale initiatives and the Business-critical model work well for regulated industries. It also provides Virtual Private Snowflake with business-critical model features and is employed in a separate Snowflake environment.
ClickHouse: ClickHouse offers three pricing models. First is the Development model, which works well for smaller workloads with 1TB storage and services like backups every 24 hours and S3-based role access. The second is the Production model, which is designed to handle production workloads and offers unlimited storage. And last is a Dedicated model which can be optimized for latency-sensitive workloads.

ClickHouse vs Snowflake: Which is Better for Data Analytics

Data analytics is a process that helps you to extract useful insights from the collected data. It helps you optimize operations and improve strategic plans by analyzing the results acquired. Choosing the ideal tool for data analytics depends on the requirements of your project. ClickHouse is particularly useful for performing real-time analytical queries over large datasets. Snowflake, on the other hand, is suitable for scenarios that require a unified platform for diverse data analytical workloads.

Factors to Consider When Choosing Snowflake vs ClickHouse

Below are some use cases that you can examine to see which tool suits your workload and integration needs:

Why Choose Snowflake?

Diversify Analytical Workloads: Snowflake Scalability enables you to handle intensive workloads from traditional business to ML and predictive analytics.
Data Sharing: Snowflake’s data-sharing functionality allows you to share your data securely among different teams. This makes it easy for you to collaborate with different departments within your organization without needing to copy or move data.
Support for Varied Data Types: You can directly load structured, semi-structured, and unstructured data, including JSON, AVRO, and Parquet, into your Snowflake data warehouse.

Why Choose ClickHouse?

Real-Time Analysis: The ability of ClickHouse to run complex analytical queries in milliseconds makes it an ideal choice for interactive applications and dashboards.
Machine Learning and GenAI: Using ClickHouse you can implement vector search and power ML models, training them at a petabyte scale.
Optimize Monitoring: You can use ClickHouse to monitor logs, events, and other time series data within your application. It also helps you detect anomalies or network or infrastructure issues through SQL-based observability.

Streamline Data Flow in Clickhouse or Snowflake Using Airbyte

While working with any storage solution, you deal with data coming from sources like databases, APIs, analytics platforms, cloud services, and more. Ensuring that this data flows seamlessly into the ClickHouse and Snowflake environment is important to maintain accuracy and consistency.

Airbyte is a powerful data movement platform that is designed to integrate between different systems, including databases, data warehouses, data lakes, vector databases, and APIs. It offers a comprehensive set of 400+ pre-built connectors. Using these connectors, you can build a data pipeline to migrate data from source to destination in just a few minutes. For instance, using the Clickhouse source connector and Snowflake destination connector, you can easily load data from ClickHouse to Snowflake with minimal coding.

Let’s see how Airbyte streamlines data flow for ClickHouse and Snowflake:

Customize Connectors: Airbyte offers you different options to build custom connectors. These options include, connector builder, low code connector development kit, Python CDK, and Java CDK.
Change Data Capture: CDC enables you to identify and replicate incremental changes from the source data into the destination. This helps you maintain data consistency within ClickHouse, Snowflake, and other destination systems.
Record Change History: Airbyte’s record change history feature allows you to maintain a record of all changes made in the source data.

Conclusion

ClickHouse and Snowflake both can be utilized for data analytical purposes, but they cater to it differently. When considering the choice between ClickHouse vs Snowflake, you should examine points such as flexibility, scalability, and query processing. While ClickHouse optimizes its data compression, columnar storage, and fast processing, Snowflake supports data analytics through auto-scaling and data-sharing capabilities.

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