Is it still necessary to understand map-reduce paradigms for machine learning on large data sets?

Is it still necessary to understand map-reduce paradigms for machine learning on large data sets?

Is it still necessary to understand map-reduce paradigms for machine learning on large data sets?  Awantik Das
Posted on May 22, 2017, 11:55 a.m.

learn machine learning

  • Spark has covered a lot of mileage in last couple of years. And, with introduction of Spark 2.0 - the internal implementation & optimization is abstracted the best possible way.
  • Spark going forward is meant for folks from R background, data scientists & others who needn’t bother about distributed computational complexity. And, still you will be doing big data computation with ease.
  • With lazy computation, sprak itself find’s the best way to execute.
  • Read a bit about Spark Dataframes & Spark’s Tungsten Engine
  • So, without any delay - get started with Spark using Python, Scala or R

Awantik Das is a Technology Evangelist and is currently working as a Corporate Trainer. He has already trained more than 3000+ Professionals from Fortune 500 companies that include companies like Cognizant, Mindtree, HappiestMinds, CISCO and Others. He is also involved in Talent Acquisition Consulting for leading Companies on niche Technologies. Previously he has worked with Technology Companies like CISCO, Juniper and Rancore (A Reliance Group Company).




Keywords : data-science hadoop spark


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