Initially open-sourced in 2012 and followed by its first stable release two years later, Apache Spark quickly became a prominent player in the big data space.
Since then, its adoption by big data companies has been on the rise at an eye-catching rate.In-memory processing
Undoubtedly a key feature of Spark, in-memory processing, is what makes the technology deliver the speed that dwarfs performance of conventional big data processing.

But in-memory processing isn’t a new computing concept, and there is a long list of database and data-processing products with an underlying design of in-memory processing. Redis and VoltDB are a couple of examples.

Another example is Apache Ignite, which is also equipped with in-memory processing capability supplemented by a WAL (write-ahead log) to address performance of big data queries and ACID (atomicity, consistency, isolation, durability) transactions.To read this article in full or to leave a comment, please click here

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