---
title: "How Ridgeline unified Iceberg and ClickHouse into one analytics layer for investment management"
description: "Ridgeline, a cloud-native investment management platform, rebuilt its performance analytics layer after Postgres could not handle the scale of larger customers. The team evaluated Redshift, SingleStore, Trino over Iceberg, and a custom in-memory engine before choosing ClickHouse for its analytical performance and lower cost. They adopted ClickHouse Cloud on AWS, using separate organizations for staging and production and per-customer services, with assistance from ClickHouse engineers. Historical data lands in Apache Iceberg via a Spark pipeline and is queried through ClickHouse, while recent data is written directly into ClickHouse. The architecture enabled Ridgeline to serve customers 10x larger than before and more than double its live customer count."
---

# How Ridgeline unified Iceberg and ClickHouse into one analytics layer for investment management

[Clickhouse](https://yomu.fyi/company/clickhouse) · Oct 14, 2026

**Type:** Problem & solution

## Summary

Ridgeline, a cloud-native investment management platform, rebuilt its performance analytics layer after Postgres could not handle the scale of larger customers. The team evaluated Redshift, SingleStore, Trino over Iceberg, and a custom in-memory engine before choosing ClickHouse for its analytical performance and lower cost. They adopted ClickHouse Cloud on AWS, using separate organizations for staging and production and per-customer services, with assistance from ClickHouse engineers. Historical data lands in Apache Iceberg via a Spark pipeline and is queried through ClickHouse, while recent data is written directly into ClickHouse. The architecture enabled Ridgeline to serve customers 10x larger than before and more than double its live customer count.

## Context

Ridgeline is a cloud-native, all-in-one investment management platform. Its performance analytics team needed to compute complex reports like inception-to-date for thousands of portfolios over decades of daily data. Postgres, used for transactional workloads, hit memory limits and could consume the entire cluster for a single report. Onboarding customers 10x larger required a new analytical solution.

## Approach / What changed

Ridgeline chose ClickHouse Cloud on AWS after evaluating alternatives. They run separate ClickHouse organizations for staging and production with per-customer services. Accounting data is written directly to ClickHouse; vendor data replicates via ClickPipes CDC; historical data lands in Apache Iceberg via a Spark pipeline and is queried through ClickHouse using a database engine. The Iceberg data is materialized into ClickHouse MergeTree tables using materialized views for fast queries.

## Takeaways

- Ridgeline evaluated Redshift, SingleStore, Trino over Iceberg, and a custom in-memory engine before choosing ClickHouse for its analytical performance and lower cost.
- Historical data from customers (billions of rows) is landed in Apache Iceberg via a Spark pipeline and queried through ClickHouse using a database engine, with materialized views optimizing query speed.
- ClickHouse Cloud’s managed service on AWS provided the security and governance needed for SOC 2 compliance, with separate organizations for staging and production and per-customer services.

## FAQ

### How does Ridgeline handle historical data from legacy providers?

Ridgeline built a Spark pipeline that ingests bulk uploads (billions of rows) and lands them in Apache Iceberg. ClickHouse queries this Iceberg data using a database engine, with materialized views in ClickHouse that pull data from Iceberg tables, allowing fast access to historical performance data alongside recent calculations.

### Why did Ridgeline choose ClickHouse Cloud over self-hosting?

Ridgeline initially self-hosted ClickHouse in staging but decided against productionizing it due to the operational burden. They chose ClickHouse Cloud for its security and governance features, especially the ability to run separate organizations per environment with clear data sharing agreements, which was cleaner than the BYOC option at the time.

### What scale of performance improvement did ClickHouse enable?

ClickHouse allowed Ridgeline to serve customers 10x larger than before and more than double the number of customers live on the platform. Reports that once took hours or would fail now complete in about a minute after changes, enabling near-real-time validation.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [ClickHouse](https://yomu.fyi/topic/clickhouse), [Fintech](https://yomu.fyi/topic/fintech), [Iceberg](https://yomu.fyi/topic/iceberg), [Postgres](https://yomu.fyi/topic/postgres)

- Source: [Clickhouse](https://clickhouse.com/blog/ridgeline-iceberg-clickhouse-cloud)
- Source URL: https://clickhouse.com/blog/ridgeline-iceberg-clickhouse-cloud
- Ingested by Yomu: 2026-10-14T18:00:14.244Z

[Read original post](https://clickhouse.com/blog/ridgeline-iceberg-clickhouse-cloud)