AWS Redshift vs. ClickHouse

Data Warehouse as a Cloud Product or as an Open Analytical Platform

Analytical data is no longer just an appendage to reporting. It forms the basis for product decisions, operational optimization, and strategic management. Thus, the question of how analytical platforms are built—and who owns them—is highly relevant.

AWS Redshift and ClickHouse address the same fundamental problem: efficiently analyzing large volumes of data. Architecturally, however, they represent two very different models. One deeply integrates analytics into a Cloud Platform. The other deliberately decouples analytical capabilities from individual providers.

AWS Redshift: Analytics as an Integrated Cloud Service

AWS Redshift is Amazon’s data warehouse offering. It is based on a distributed, columnar architecture optimized for analytical SQL queries on large datasets. Typical data sources include S3, Kinesis, or RDS, with queries executed via SQL and scaling achieved through fixed node types or Redshift Serverless.

AWS handles operations, patching, and high availability, significantly reducing operational overhead. For organizations that already generate and process their data in AWS, Redshift is quickly deployable and well-integrated.

However, this integration defines the architectural framework.

Platform Logic Instead of a Neutral Analytics Layer

Redshift is fully confined to AWS—both infrastructurally and functionally. While storage and compute are logically separated, they remain tied to AWS services, billing models, and network topologies.

Performance optimizations follow Redshift-specific mechanisms: Distribution Styles, Sort Keys, Workload Management. These concepts work well within Redshift but are not portable. Query behavior, cost structure, and scaling are thus closely linked to the platform logic.

Switching environments is technically possible but practically cumbersome. Data pipelines, data models, optimizations, and operational processes need to be rethought. Analytics thus becomes part of the cloud lock-in.

ClickHouse: Analytics as Independent Infrastructure

ClickHouse takes a fundamentally different approach. As an open-source, columnar analytics database, it is designed for extremely fast queries over very large datasets—particularly for log, metric, and event data.

Data is stored highly compressed, and queries are massively parallelized. ClickHouse can be operated as a single node or as a distributed cluster, on-premises or in any cloud environment. The architecture is open, transparent, and fully controllable.

Here, analytics is not consumed but built.

Control Over Architecture and Data Flow

The crucial difference lies in control. ClickHouse deliberately separates analytical capabilities from cloud-specific services. Data pipelines, replication, sharding, and retention are managed through open configurations, not provider defaults.

Kubernetes deployments are established, as is operation in hybrid or multi-cloud scenarios. Queries remain SQL-like, without relying on proprietary optimization mechanisms that only function within a specific product.

The analytical platform remains flexible—regardless of where the data originates.

Deliberate Design Instead of Abstracted Convenience

This approach requires deliberate architectural work. ClickHouse is not a fully abstracted managed warehouse. Schema design, partitioning, replication, and resource planning are part of the responsibility.

In return, transparency is achieved. Costs, performance, and data retention are traceable. Storage grows horizontally, query performance scales through cluster structures rather than predefined service tiers. Optimization occurs architecturally—not through higher pricing tiers.

This difference is crucial, especially with rapidly growing data volumes.

Relevance for Data-Intensive Platforms

In data-intensive platforms—such as for observability, product analytics, or event-driven systems—the difference becomes particularly clear. Redshift offers an integrated warehouse within AWS. ClickHouse establishes an independent analytics layer.

This layer can be consistently integrated into Kubernetes and open-source stacks, independent of the cloud provider. Analytics thus becomes part of the platform architecture, not an isolated cloud service.

Condensed Comparison

When Each Approach Makes Sense

AWS Redshift is suitable for:

• clearly AWS-centric data platforms
• traditional BI and reporting workloads
• low portability requirements
• focus on minimal operational effort

ClickHouse is suitable for:

• data-intensive platforms (logs, events, metrics)
• Kubernetes and open-source-centric architectures
• multi-cloud or hybrid scenarios
• high demands on performance and cost control
• long-term analytical strategies

Conclusion

Analytical data is not a byproduct. It determines how quickly systems can be understood, optimized, and further developed.

AWS Redshift ties analytical capability to a platform. ClickHouse keeps it open and controllable.

The difference is not primarily technical but strategic. Binding analytics to a provider also binds insight and development speed. Operating it as an open platform retains control over performance, costs—and future viability.