We discuss here how PrestoDB can be used to make Spark better by apriori unification and Spark to make PrestoDB better by resource management. PrestoDB is one of the tool that I use in my daily tech life since 2013, from when its open sourced. I have built products on the top of PrestoDB in my past, conducted many research on it collaborating with companies like Intel and Hewlett Packard. This recent innovation in PrestoDB is going to make its use more addictive to the data community.

I have coined the term “Apriori Unification Pattern” some years ago for data engineering. The combination of Presto and Spark ultimately realizes this dream. This article explains two approaches: 1) Ability to perform complex pre unification of data through complex queries in Presto prior to execution on Spark and 2) one of the most powerful additions in the latest release of PrestoDB — the ability tun itself on Spark, making it fault tolerant. Let me give you a conceptual walkthrough of what does this mean and the relevant value to data engineering or data science. I take this opportunity to thank Andrill Rosa for the fantastic work on prestodb on Spark. I also happen to play a small role as a committer in extending the Presto-spark package for support of more connectors and testing it.

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