Upgrading your SQL engine: The first migration pathway from Hadoop to Starburst

Enterprises are increasingly moving away from traditional Hadoop architectures due to challenges with performance, maintenance, and scalability, opting instead for modern data lakehouses powered by SQL engines like Starburst's Trino. The Hadoop Distributed File System (HDFS) struggles with large-scale datasets, leading to inefficiencies and increased costs, compared to cloud object storage solutions that separate compute and storage for scalable, cost-effective data management. Hadoop's SQL-like querying tool, Hive, simplifies data processing but remains limited in speed and scalability, prompting organizations to explore alternatives like Starburst, which leverages Trino's high-performance, massively parallel processing capabilities. This transition enhances query performance, security, and governance while reducing operational costs, allowing seamless integration with existing data ecosystems and supporting a wide range of data sources. By adopting a Trino-based SQL engine, organizations like Optum have reported substantial improvements in query speed and infrastructure cost savings.

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