Guide

A Data Leader's Guide: Migrating from Hadoop to an Iceberg-Powered Lakehouse

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Who This is For:

Data architects, infrastructure leads, and IT decision-makers responsible for modernizing legacy Hadoop or HDFS environments and building AI-ready data infrastructure at enterprise or petabyte scale.

Key Takeaways
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ONS exascale-native architectures deliver 36 petabytes of usable capacity per rack at 900 watts per petabyte, enabling organizations to deploy an exabyte of AI-ready storage today instead of waiting 24-36 months for new data center capacity.

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AIStor Tables embeds the Iceberg REST catalog natively into the storage binary, removing the separate catalog service from the architecture and enabling Spark, Trino, Dremio, and Starburst to query data without additional infrastructure.

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Customers including a leading financial group achieved 60 percent+ cost-to-performance improvement post-migration, while NCR saw 30x faster dashboard performance by pairing AIStor with modern query engines.

HDFS tightly couples storage and compute at a time when AI compute demand doubles every 3.4 months. There is no modernization path that preserves that coupling; this guide argues the only viable direction is object-native storage, and it provides a concrete roadmap for getting there. The target architecture is a data lakehouse built on MinIO AIStor and Apache Iceberg, with AIStor's native Iceberg REST catalog eliminating the need for a separate metadata database and enabling structured and unstructured data to live in a single coherent store. On-premises deployments on this architecture become economically favorable over public cloud at approximately 5 PB of hot data. The guide covers a five-step phased migration approach: beginning at the query layer, adopting open table formats, implementing dual ingestion, migrating data using Hadoop distcp and mc mirror, and decommissioning Hadoop after workloads are validated. Throughput and sort benchmarks versus HDFS are included.

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