As data increased in volume, velocity, and variety, so, in turn, did the need for tools that could help process and manage those larger data sets coming at us at ever faster speeds.
As a result, frameworks such as Apache Spark and Apache Flink became popular due to their abilities to handle big data processing in a fast, efficient, and scalable manner.
But we often find that sometimes it can be difficult to understand which use cases are best suited for Spark as well as for Flink (or even which might be suited for both).
In this article, we’ll discuss some of those unique benefits for both Spark and Flink and help you understand the difference between the two, and go over real use cases, including ones where the engineers were trying to decide between Spark vs. Flink.
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About me:
I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.
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