Selecting an analytical database is particularly challenging now. As data grows each year by more than 20% (according to Edge Delta) and the requirement for real-time analytics increases, companies need precise, effective, and adaptable solutions. In the OLAP ecosystem, Snowflake and ClickHouse are hard to miss.
In this blog post, we will analyze ClickHouse vs Snowflake in terms of features, performance, and pricing. For example, benchmarks show ClickHouse query speed to be up to 50% faster and the analytics cost for real-time scenarios up to 60% lower than the competition.
P.S. We gain nothing from this comparison, other than serving what we believe is an underserved question out there. The only thing we’re promoting is clarity (and maybe our love for great analytics tools).
Before we jump into comparisons, let's look at what makes these platforms unique.
What is ClickHouse?
ClickHouse is an exceptionally fast and powerful open-source database management system designed for real-time Online Analytical Processing workloads. It was developed by Yandex and is now known for managing enormous amounts of data and executing queries at astonishing speeds. ClickHouse’s cost efficiency and scalability make it an industry favorite for companies seeking real-time analytics and insights. It is easily deployed on-premises, in the cloud, or through its managed service, ClickHouse Cloud.
Key Features:
Columnar Storage: By storing data in columns instead of rows, ClickHouse significantly improves the speed of analytical queries. It is very useful for quick data processing and analysis because of its ability to handle large datasets.
Superior Compression: This system allows companies with a greater level of data storage without compromising performance due to its advanced compression techniques.
Materialized Views: ClickHouse enhances its performance with materialized views that store the results of the pre-executed queries removing the recurrence of running complex queries on large datasets.
Real-Time Analytics: Perfect for high-ingestion use cases such as IoT, observability, user behavior tracking, and financial analytics, ClickHouse excels at processing and analyzing data streams in real time. This enables businesses to gain actionable insights as events occur.
What is Snowflake?
Snowflake is a fully managed and cloud-based data platform which can accommodate an organization's varied data requirements. It uses a unique architecture which provides unmatched scalability, and it is the preferred option for highly sophisticated BI and multi-cloud implementations. Snowflake is excellent at seamlessly integrating both structured and semi-structured data, making it effortless for modern organizations to become data-driven.
Key Features:
Elastic Scaling: Powered by virtual warehouse model within Snowflake, it provides a self adjusting resource model that dynamically addresses the current workload.
Support for Semi-Structured Data: Compatible with formats like JSON, Parquet, and Avro, among others can be integrated with ease.
Built-In Data Sharing: Simplifies collaborative efforts within an organization and with outside groups.
Multi-Cloud Capabilities: Data can be hosted on AWS, Google Cloud, or Azure making it flexible and reliable.
Feature Comparison
Architecture
ClickHouse: Storage and computation are highly integrated, making it very effective for real time analytics and low latency queries. Recent enhancements to ClickHouse Cloud support the separation of compute resources which allows for improved isolation and more economical scaling resulting in better cost efficiency.
Snowflake: Built with a fully decoupled architecture, separating storage, compute, and cloud services. Scalable virtual warehouses enable independent scaling, making it ideal for diverse, high-concurrency workloads.
Performance
ClickHouse: Optimized for speed with sub second query responses and high throughput ingestion. Snowflake benchmarked OLAP workloads on ClickHouse and it performed better, making it great for real time dashboards and time-series analytics.
Snowflake: Excels in concurrent BI workloads and complex, ad-hoc queries. While not optimized for pure OLAP workloads, they excel in diverse and high-concurrency situations.
Data Types and Storage
ClickHouse: Structured data is covered with efficient columnar storage and advanced compression making it cost effective for time series and analytical workloads.
Snowflake: Supports both structured and semi-structured data (ex: JSON, Parquet). Micro-partitioned storage along with hybrid processing provides broad analytics capabilities.
Scalability
ClickHouse: Sharding replicates data across nodes, allowing horizontal scaling. Cloud offerings provide horizontal and vertical scalability without limits.
Snowflake: Provides near boundless scalability with independent storage and compute scaling. Multi-cluster virtual warehouses provide seamless scaling to heightened concurrency.
Concurrency
ClickHouse: Capable of supporting high-ingest analytical workloads due to recent improvements to workload isolation and concurrency. May not be as accommodating for traditional BI with high user concurrency.
Snowflake: Maintains industry-leading concurrency with automatic resource scaling and multi-cluster architecture, ensuring consistent performance during demanding periods.
Pricing Model
ClickHouse: Transparent and cost-effective with open-source self-hosting options or resource-based pricing for managed cloud services.
Snowflake: Pricing can become exorbitant at scale due to the pay-per-use model for compute and storage resources. Though, auto-scaling and compute suspension features help manage cost for variable workloads
We have created a simple summary table so that you can compare more easily:
Category | ClickHouse | Snowflake |
Architecture | Tightly coupled (now supports compute separation in cloud); columnar storage | Decoupled storage & compute; multi-layer cloud-native |
Performance | Sub-second queries, high ingest, OLAP-optimized | Optimized for concurrency & BI workloads |
Scalability | Horizontal (sharding, replication); improved vertical/horizontal scaling in cloud | Elastic, independent scaling; multi-cluster |
Data Formats | Structured, time-series | Structured & semi-structured (JSON, Parquet) |
Cost Model | Lower costs for sustained workloads | Credits-based; may increase with heavy workloads |
Deployment | On-premises or cloud | Cloud-only (AWS, Azure, GCP) |
Use Case Scenarios
Choose between these two platforms wisely by understanding your company's requirements. Here’s a look at when ClickHouse and Snowflake shine:
When to Choose ClickHouse
Real-Time Analytics: Ideal for time-critical use cases, such as IoT or observability dashboards.
High-Throughput Data: Best for log and telemetry data streams that require constant data flow.
Cost-Sensitive Use Cases: Ideal for startups or SMBs looking for an affordable, high-performance solution.
When to Choose Snowflake
Enterprise BI: Perfect for reporting and extensive BI projects.
Multi-Cloud Needs: Covers all requirements for deployment in multiple clouds.
Semi-Structured Data: Great for companies managing diverse file types, including JSON and Parquet.
Pros and Cons
ClickHouse
Pros | Cons |
Open-source and cost-effective | Limited concurrency and join capabilities compared to Snowflake |
Extremely fast for real-time analytics | Requires more technical expertise for setup and cluster management (if self-hosted) |
Flexible deployment (self-hosted/cloud) | Less robust support for complex joins |
Efficient storage and compression | Not ideal for transactional use |
Snowflake
Pros | Cons |
Fully managed platform with minimal operational overhead | Higher costs for sustained/high usage |
Fully managed, minimal operational effort | Potential vendor lock-in due to its proprietary model |
Handles structured & semi-structured data | Usage-based pricing can be unpredictable |
Strong BI tool and data-sharing integration | No built-in visualization tools |
Making the Decision
Choosing between ClickHouse and Snowflake comes down to your analytics needs, budget, and team expertise. Here’s how to decide:
Workload Type
ClickHouse: Best for heavy, real-time analytics and ultra-fast, low-latency dashboards.
Snowflake: Ideal for multi-user, enterprise BI and robust data sharing.
Budget
ClickHouse: Cost-effective for always-on, high-ingest workloads.
Snowflake: Flexible on-demand pricing suits bursty workloads, but costs can add up with extended use.
Team Expertise
ClickHouse: Offers deep customization but requires strong technical skills for setup and management.
Snowflake: Easier for SQL-proficient teams, offering a managed, low-maintenance solution.
Use Case Priorities
Choose Snowflake for seamless data sharing, collaboration, and BI tool integrations.
Choose ClickHouse for real-time analytics performance and cost efficiency.
Key Considerations
Test Before You Commit: You can take advantage of both Snowflake and ClickHouse free trials to test them out risk-free. Simulating your workloads will reveal how usable, performant, and cost-efficient they are.
Plan for Migration: If you're switching platforms, prepare your data pipelines ahead of time. Both platforms provide tools and support to simplify the migration process.
Conclusion
ClickHouse is the go-to choice for speed, efficiency, and affordability in real-time analytics. On the other hand, Snowflake excels in scalable, collaborative BI environments where ease of use and robust data sharing are key.
Evaluate your data architecture, scalability needs, and team skills to determine the best fit for your organization.
I hope you found this blog insightful and informative. We’d love to hear your feedback - your input helps us create even more valuable content for you!
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