Transitioning from Hadoop to modern lakehouses

As organizations aim to maximize their data's potential, transitioning from Hadoop to modern lakehouses with Starburst addresses the limitations of legacy systems, such as performance bottlenecks and high operational overhead. Hadoop's traditional architecture, reliant on components like HDFS and SQL engines such as Hive and Impala, is robust for batch processing but struggles with real-time analytics and high-concurrency workloads. Starburst's integration introduces Trino as a powerful query engine and Apache Iceberg for advanced data management, transforming Hadoop into a modern, efficient lakehouse model. This shift supports real-time data ingestion, automated data management, and unified governance, offering improved performance, cost efficiency, and scalability. Organizations can choose from a phased approach, including SQL engine upgrades, on-premises modernization with Dell, or cloud-centric solutions with Starburst Galaxy, to meet specific needs and regulatory requirements. For instance, Optum's deployment of Starburst resulted in queries running ten times faster and significant cost savings, illustrating the tangible benefits of transitioning to a modern lakehouse architecture.