11. Ease of use and administration
ClickHouse and BigQuery offer user-friendly interfaces and management features, but they differ in installation and setup. ClickHouse requires more manual configuration and setup than BigQuery, offering a more streamlined installation process. However, once ClickHouse is up and running, its user experience is highly instinctive and easy to navigate. On the other hand, BigQuery provides a more polished and user-friendly interface right from the start.
Both platforms offer query optimization techniques, but ClickHouse’s unique columnar storage architecture allows for faster query response times and more data processing.
12. Security and compliances
When it comes to security, BigQuery ClickHouse both offer robust features to ensure the protection of data. ClickHouse Cloud provides authentication, authorization, and encryption capabilities, allowing users to control and secure access to their data against unauthorized access. Similarly, BigQuery also offers authentication and authorization mechanisms and encryption at rest and in transit to safeguard sensitive information.
In terms of compliance certifications and standards, both platforms have made efforts to meet industry requirements. For example, Clickhouse achieves GDPR compliance, ensuring that it adheres to the regulations set forth by the European Union for the protection of personal data.
Additionally, ClickHouse also offers features such as access control lists (ACLs) and role-based access control (RBAC) to enhance data security further. On the other hand, BigQuery has obtained various compliance certifications, including ISO 27001 and SOC 2, demonstrating its commitment to maintaining a high level of security and compliance.
13. Use cases
BigQuery is a serverless data warehouse suitable for internal BI and reporting and is tightly integrated with GCP, making it convenient for Ad-Hoc analytics and ML use cases. However, it makes resource allocation decisions, making it not suitable for operational use cases and data apps with consistent and predictable performance.
Clickhouse, designed for low-latency query execution runtime, is suitable for engineering managed operational use cases and customer-facing data apps but not for general-purpose data warehouses, Ad-Hoc analytics, or ELT.
14. Integration with other tool
Some popular data analysis, visualization, and data pipeline tools that ClickHouse integrates with include Apache Spark, Tableau, Power BI, and Apache Kafka. ClickHouse provides connectors, APIs, and ecosystem partnerships that enhance interoperability with these tools, allowing users to integrate ClickHouse into their existing data workflows seamlessly.
Comparatively, BigQuery also offers a wide range of connectors and APIs for integration with various tools, such as Google Data Studio, Looker, and Apache Beam. Both ClickHouse and BigQuery prioritize compatibility and integration capabilities to ensure smooth data analysis and exploration experiences for users.
15. Community and support
The ClickHouse Cloud community is rapidly growing, with active users and developers sharing ideas and experiences through online communities and forums. The platform’s documentation is comprehensive and regularly updated, providing detailed information on various aspects. Platform providers are responsive to user queries and actively engage with the community, ensuring access to support.
Meanwhile, BigQuery’s large and active community offers ample developer support and following resources, with users sharing knowledge and best practices. The well-structured and regularly updated documentation makes it easy for users to find relevant information and troubleshoot issues.
16. Limitations and constraints
When comparing BigQuery and ClickHouse, it’s crucial to consider their limitations and constraints. ClickHouse has data size limitations, making it unsuitable for large datasets. BigQuery has a higher data size limit, allowing for the analysis of larger volumes. ClickHouse may have query complexity restrictions, while BigQuery handles complex queries efficiently. Data consistency can be challenging in ClickHouse, while BigQuery offers strong guarantees.
Users should know about these limitations and consider their needs when choosing between the two platforms. BigQuery may offer better performance and scalability for larger datasets and more complex analytical needs. Understanding the trade-offs and limitations of each platform helps users make informed decisions and maximize the value from their data analysis efforts.
17. Deployment and operations
ClickHouse offers a variety of deployment options, including on-premises, Clickhouse cloud based, and containerized deployments. This flexibility allows many to choose the best deployment method for their needs and infrastructure. On the other hand, BigQuery are fully managed services provided by Google Cloud, meaning deployment is simplified, and users do not need to worry about infrastructure management.