7 big data tools to ditch in 2017

We’ve been on this big data adventure for a while. Not everything is still shiny and new anymore. In fact, some technologies may be holding you back. Remember, this is the fastest-moving area of enterprise tech — so much so that some software acts as a placeholder until better bits arrive.

Those upgrades — or replacements — can make the difference between a successful big data initiative and one you’ll be living down for the next few years. Here’s are some elements of the stack you should start to think about replacing:

1. MapReduce. MapReduce is slow. It’s rarely the best way to go about a problem. There are other algorithms to choose from — the most common is DAG, of which MapReduce can be considered a subset. If you’ve done a bunch of custom MapReduce jobs, the performance difference compared to Spark is worth the cost and trouble of switching.

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