---
title: "Iceberg"
description: "2 posts about Iceberg, summarised, each linking to the original."
---

# Iceberg
> 2 posts about Iceberg, summarised, each linking to the original.

## Articles

### [How Ridgeline unified Iceberg and ClickHouse into one analytics layer for investment management](https://yomu.fyi/post/how-ridgeline-unified-iceberg-and-clickhouse-into-one-analytics-layer.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Oct 14, 2026
- Type: Problem & solution

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.


### [Your data, your storage, your rules: a 2026 guide to storing Unity Catalog managed tables](https://yomu.fyi/post/your-data-your-storage-your-rules-a-2026-guide-to-storing-unity-catalo.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Elizabeth Bowman, Isabel Dong
- Published: Sep 29, 2026
- Type: Explainer

Unity Catalog managed tables let you control where your data physically resides while keeping it in cloud storage you own, such as S3, ADLS, or GCS, unlike platforms that store tables in provider-controlled storage. You set managed storage locations at the metastore, catalog, or schema level, with the most specific level winning, and can change where new tables land using ALTER CATALOG or ALTER SCHEMA commands. Managed tables use open formats including Iceberg and Delta, and external engines such as Apache Spark, Flink, and Trino can read and write through Iceberg REST Catalog and Unity Catalog open APIs. When converting an external table to managed, Databricks copies the data and transaction log into the managed storage location you have set. This approach enables a single source of truth across analytics and AI workloads while satisfying compliance, cost allocation, and data residency requirements.