Download - Implementation challenges in Big Data - Dr. Nilesh Karnik

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Copyright 2013 RESTRICTED CIRCULATION

Footer Option 2Implementation Challenges in Big Data Analytics

• Dr. Nilesh N. Karnik

Copyright 2013 RESTRICTED CIRCULATION 2

The Challenge of BIG Data

ADVANCED Analytics

SOLUTIONS in the Pipeline

What we will discuss



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Big Data : Distributed Processing

OLD IDEA NEW IDEA

!



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EXAMPLE 1: Task of storing books on a shelf

Simple, right?

Image source Flickr. Image copyright belongs with original artist.



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EXAMPLE 1: Task of storing books on a shelf

And now?




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EXAMPLE 2 : Summarizing a Report

SUMMER PROJECT REPORT

Simple, right?



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EXAMPLE 2 : Summarizing a Report

And now?



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EXAMPLE 3 : Baking a Cake

Simple, right?And now?

Image source PINTEREST. Image copyright belongs with original artist.



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Advanced Analytics

• Well developed tool set for “small data” environment

• Challenges in Big Data environment



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Advanced Analytics: MapReduce Difficulties

ITERATIVE




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INCREMENTAL PROCESSING REQUIRES RESTART




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BATCH LEARNING SCANS ALL DATA IN ONE GO



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Some Solutions Data Scientists are working on

New frameworks• E.g., HaLoop*, PrIter# (Extensions of Hadoop)

• Percolator$ (Proprietary Google framework)

* Y. Bu, B. Howe, M. Balazinska, and M. Ernst, “HaLoop: Efficient iterative data processing on large clusters”, VLDB, 2010.# Y. Zhang, Q. Gao, L. Gao and C. Wang, “PrIter: A distributed framework for prioritized iterative computations”, SoCC, 2011. $ D. Peng and F. Dabek, “Large-scale incremental processing using distributed transactions and notifications”, OSDI, 2010



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Some Solutions Data Scientists are working on

Smarter algorithms / Different implementations

• Random forest

• Parallelized Stochastic Gradient Descent



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