Snowflake is taking aim at the operational burden of managing data lakehouse storage with its new Snowflake Storage for Apache Iceberg™ tables, now generally available on AWS and Azure. The move promises to combine the open interoperability of Apache Iceberg tables on Snowflake with Snowflake's own resilient, zero-management storage infrastructure.
Companies working on this
Profiles of the companies named in this story, with founding year, headquarters, and a short description from our database.
A cloud-based data platform enabling data warehousing, data lakes, data engineering, and data sharing.
- Founded
- 2012
- Location
- Bozeman, Montana, United States
- Valuation
- $12.4B
The promise of an open lakehouse architecture has often been hampered by the reality of "self-managed" storage. This typically means data teams spend excessive time on cloud bucket configuration, policy management, and risky maintenance, creating a hidden operational tax.
Eliminating Storage Complexity
Traditionally, using Iceberg meant data engineers were responsible for complex tasks like configuring IAM roles and ensuring external engines stayed synchronized with table versions. Snowflake Storage for Apache Iceberg™ tables removes this friction by allowing Iceberg tables to be hosted directly on Snowflake-managed infrastructure.
To administrators, these tables appear as native Snowflake data. To external engines like Spark or Trino, they present as standard, high-performance Iceberg tables.
Built-in Data Integrity
Self-managed storage introduces fragility, particularly when mistakes occur. Accidentally deleting critical metadata folders or manifest files can render an Iceberg table inconsistent, leading to hours or days of recovery work.