TECHNICAL WHITEPAPER
6 Guidelines for Evaluating Data Lake ETL Tools
There are many different options for data lake ETL, and deciding on the right one for your data project could be challenging. Whether you choose an open-source (Spark/Hadoop/Flink), proprietary, or a custom-developed ETL tool, it’s important to choose a platform that will be able to address your specific hurdles.
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What will you learn from this whitepaper?
- Understand the differences between data lake ETL and a traditional database ETL.
- Discover how Upsolver performs stateful operations in-memory and supports joins and aggregations.
- Learn how to dramatically reduce the complexity and time-to-market of big data projects.
- Real-life case studies and architecture examples using Upsolver, Amazon S3 and other AWS data lake tools.
Powering data lakes for data-intensive companies
