Fully managed in the cloudStarburst GalaxySelf-managed anywhereStarburst Enterprise
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Fully managed in the cloud
Hadoop is a popular open-source framework for storing and processing large-scale data on-premises. However, Hadoop comes with many practical challenges, such as high maintenance costs, complex administration, scalability issues, and a lack of cloud-native features.
The modern data lake is a cost-effective, performant, and future data architecture that is built on an open foundation. A modern data lake strategy can help simplify and streamline the migration from Hadoop to the cloud. With Starburst, organizations can migrate from Hadoop to the cloud faster, more easily, and cheaply.
Starburst Galaxy is powered by open source Trino and is designed for analyzing large and complex data sets in and around your cloud data lake – from gigabyte to petabyte scale.
Gravity is a universal discovery, governance, and sharing layer in Starburst Galaxy that enables the management of all your data assets through a easy-to-use interface.
Every data store is a first-class entity in Starburst Galaxy. Use the architecture that meets your needs today and easily change it when new needs emerge.
Begin with assessing your existing Hadoop setup, introducing Trino as the compute engine. Analyze data specifics, workflows, dependencies, and desired outcomes to define project scope and objectives.
Compare cloud options based on features, compatibility, and costs. Match these with migration goals to identify the optimal cloud solution, potentially spanning multiple platforms.
Map out storage, compute, and analytics layers. Choose scalable storage (e.g., Azure Data Lake, Amazon S3), compute service (Trino and Hive), analytics tools, and account for security, governance, and observability.
Prioritize batch migration over simultaneous transfer for efficiency. Minimize disruption, monitor the process, and ensure business continuity by maintaining data federations between legacy and new systems.
Prepare data by cleansing, transforming, and validating it. Choose migration tools like Azure Copy, AWS Transfer, or BigQuery Data Transfer, and ensure incremental data movement for accuracy. Consider managed options or manual scripts.
“Gartner clients have described plans to replace broad, complex suites of jobs running against large, optimized data warehouses by “moving it to Hadoop.” Not surprisingly, many of these projects have not succeeded.”
Learn how data teams win the data migration battle with Starburst while lowering costs, gaining more control, and increasing business insights without being locked into proprietary tools.
Learn the rationale behind migrating from Hive to Iceberg, the steps needed to complete a successful migration, and highlight some of the benefits of doing so.
Up to $500 in usage credits included