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Lakehouse Management

Apache Iceberg is an open-source table format designed for big data analytics, offering significant advantages over traditional data lake storage. It provides schema evolution, hidden partitioning, time-travel queries, and ACID transactions, making it a powerful foundation for modern lakehouses. While Iceberg brings major flexibility and performance benefits, it also introduces challenges in monitoring, management, and optimization. As datasets grow, organizations struggle with small file accumulation, inefficient partitioning, and suboptimal sort orders, leading to slower queries and rising costs.

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The Ryft Platform

Ryft is the Intelligent Iceberg Management Platform, designed to continuously monitor, manage, and optimize your Iceberg Lakehouse. Ryft provides full visibility into your lakehouse performance and usage, identifying inefficiencies in data layout and query patterns. It then automatically tunes your environment to improve query performance and reduce costs, ensuring your lakehouse operates at peak efficiency with minimal manual maintenance.

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With Ryft, data teams can:

  • Monitor and audit their entire Iceberg lakehouse from a single dashboard.
  • Reduce operational overhead by automating maintenance & optimization.
  • Accelerate queries by up to 10x by optimizing table layouts based on query and ingestion patterns.
  • Cut infrastructure costs by up to 50% by identifying lakehouse waste and automating table maintenance and cleanup procedures.