Ridgeline Unites Iceberg & ClickHouse for Analytics Power

Beyond Postgres: A Data Evolution

The article effectively highlights Ridgeline's successful migration from a Postgres-centric architecture to a hybrid approach leveraging ClickHouse Cloud and Iceberg for enhanced performance analytics. The key takeaway is the significant scalability and speed improvements, enabling them to serve larger clients and double their customer base. The choice of ClickHouse Cloud over self-hosting, driven by security and governance needs (SOC 2 compliance), is a crucial point for financial services firms. The integration of Iceberg for historical data, queried via ClickHouse's connector, demonstrates a practical solution for handling vast historical datasets economically. This hybrid strategy allows for recent, high-velocity data to reside in ClickHouse for immediate analysis, while cost-effective Iceberg stores long-term historical archives, accessed on demand.

However, while the performance gains and business impact are clearly articulated, the article could delve deeper into the technical intricacies of the 'adapters' built by Erin's team to pull Iceberg data into ClickHouse on demand. Understanding the architecture and potential performance bottlenecks of these custom connectors would offer more comprehensive technical insight. Additionally, while ClickHouse's affordability is mentioned as a factor against Redshift, SingleStore, and others, specific cost comparisons or ROI figures would strengthen this claim. The article also touches upon built-in AI, but this aspect remains largely unexplored, leaving a potential area for future discussion and demonstration of how AI is truly integrated beyond just a mention. The reliance on materialized views as the backbone is a strong architectural pattern, but detailing specific optimizations or challenges encountered during their implementation would add further value.

Key Points

• Ridgeline migrated from a Postgres-centric architecture to a hybrid model using ClickHouse Cloud and Iceberg for performance analytics.
• This shift resulted in a 10x increase in the size of customers they can serve and more than doubled their live customer base.
• ClickHouse was chosen for its analytical performance and affordability over alternatives like Redshift, SingleStore, and custom in-memory engines.
• ClickHouse Cloud was selected for its robust security and governance features, crucial for SOC 2 compliance in financial services.
• Iceberg is used for economical storage of vast historical data, queried on demand via ClickHouse's connector, complementing recent data stored directly in ClickHouse.
• Materialized views are a core component, transforming write-side data into optimized read-side views for efficient reporting.

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📖 Source: How Ridgeline unified Iceberg and ClickHouse into one analytics layer for investment management