Ryft provides an automated management layer for Apache Iceberg tables that handles usage‑driven compaction, retention, and GDPR/CCPA compliance without manual intervention. It integrates via a one‑click connector with any catalog, storage backend, or query engine, offering resource‑aware scheduling, policy‑driven tiering, and real‑time monitoring to reduce commit conflicts and improve query performance.
Funding
$8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Data engineering teams must manually schedule compaction, enforce retention, and ensure GDPR/CCPA compliance for Apache Iceberg tables, which leads to high operational overhead, frequent commit conflicts, and sub‑optimal query performance as workloads scale.
Solution
Ryft delivers a fully automated management layer for Iceberg tables that continuously learns query and ingestion patterns and applies usage‑driven compaction, retention, and cleanup without human intervention. The platform integrates with any catalog, storage backend, or query engine via a one‑click connector, enabling unified governance across batch, streaming, and CDC pipelines. Built‑in compliance modules automatically purge personal data from snapshots, backups, and orphan files to maintain GDPR and CCPA standards. Real‑time workload‑aware scheduling reduces commit conflicts by up to 99 % and optimizes data layout for faster query execution, typically delivering 3‑4× speed improvements while cutting lakehouse TCO by roughly 45 % within a month. Engineers gain end‑to‑end visibility through dashboards that track ingestion rates, schema evolution, and performance metrics, allowing them to focus on analytics rather than table maintenance.
Target Audience
The primary customers are data engineering and lakehouse operations teams at enterprises that run large‑scale Apache Iceberg deployments, especially those supporting ML/AI workloads and multi‑engine analytics environments.
Features
- Usage‑based compaction engine that auto‑tunes based on query frequency, data size, and column distribution
- Resource‑aware scheduling that aligns maintenance windows with cluster capacity to avoid contention
- Automated GDPR/CCPA enforcement that deletes personal data across snapshots, soft deletes, and backup stores
- Policy‑driven tiering and retention controls for cost‑effective storage lifecycle management
- Integrated backup and disaster‑recovery service with point‑in‑time restore for Iceberg tables
- One‑click connector to existing catalogs, object stores, and multiple query engines (Spark, Trino, Flink, etc.)
- Centralized monitoring console providing real‑time metrics on ingestion rates, schema changes, and commit conflict rates