6 Guidelines for Evaluating Cloud Data Integration Tools

There are many different options for integrating big or streaming data, 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.

Read the 6 guidelines to help you evaluate tools based on the critical capabilities that a data integration tool must support in 2021.


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What will you learn from this whitepaper?

  • Understand the differences between big data integration 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 tools.

Powering data lakes for data-intensive companies


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