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THE STARBURST DATA LAKEHOUSE
The fastest path from Hadoop to data lakehouse
Modernize your data lake strategy with 10x the performance at a fraction of the cost
Why a data lakehouse?
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 data lakehouse is a cost-effective, performant, and future data architecture that is built on an open foundation. A data lakehouse 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.
Why Starburst?
Interactive analytics at petabyte scale
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.
- Run internet-scale SQL workloads with the power of Trino
- Accelerate interactive queries 40%+ with Warp Speed – proprietary smart indexing and caching technology
- Run long-running workloads without the fear of query failure using fault-tolerant execution
Intuitive and powerful user experience
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.
- Find data that already exists faster and only spend compute resources on net new data requests
- Secure data based on any input – where it lives, how it is structured, what it contains, or which teams it is relevant to
- Streamline sharing and collaboration between producers and consumers via data products
Single point of access and governance
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.
- Connect to 20+ cloud data sources
- Optimized for Apache Iceberg but works with all modern table and file formats, including Delta Lake and Apache Hudi
- Analyze your data cross-region and cross-cloud (coming soon) from a single query
5 key considerations for a successful migration
Evaluate current environment
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.
Select cloud platform
Compare cloud options based on features, compatibility, and costs. Match these with migration goals to identify the optimal cloud solution, potentially spanning multiple platforms.
Design cloud architecture
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.
Plan data migration
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.
Agile migration execution
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.”
Merv Adrian and Rick Greenwald, Gartner
Migration resources
Interested in learning more?
A single point of access to all your data
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