Is Hadoop a necessity for Data Science

1. www.edureka.co/r-for-analytics
www.edureka.co/big-data-and-hadoop
Is Hadoop a necessity for data Science ?

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Today we will take you through the following:
 What is Big Data & Hadoop?
 What is a Data Product?
 What is Data Science?
 Why Hadoop for Data Science?
 Is Hadoop a necessity for Data Science?
AGENDA

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What is
Big Data & Hadoop?

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BIG DATA
Big data is a popular term used to describe the exponential
growth of data.
Big Data can be either Structured data or Unstructured data
or a combination of both.
Big Data

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BIGDATA
3 V’s (Volume, Variety and Velocity) are three defining properties or dimensions of Big Data.

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HADOOP
Hadoop is a programming framework
that supports the processing of large
data sets in a distributed computing
environment.
Hadoop was the first and still
the best tool to handle Big
Data.

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A BRIEF HISTORY OF HADOOP

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HADOOP:- HDFS & MAP-REDUCE
Most efficient for Large-Scale Storage & Processing
 HDFS: Distributed file system
Self-Healing Data store
 MAP-REDUCE: Distributed computation framework
that handles the complexities of distributed
programming

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KEY TO HADOOP’S POWER
 Computation co-located with data
Data and computation system co-designed and co-developed to work
together
 Process data in parallel across thousands of “commodity” hardware
nodes
Self-healing; failure handled by software
 Designed for one write and multiple reads
There are no random writes
Optimized for minimum seek on hard drives

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What is a Data product?
“A software system whose core functionality
depends on the application of statistical analysis
and machine learning to data.”

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Example #1: People you may know

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Example #2: Spell Correction

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What is
Data Science?

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DATA SCIENCE
#1: Extracting deep meaning from data
(data mining; finding “gems” in data)

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Common Data Science tasks

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DATA SCIENCE
#2: Building Data Products
(Delivering Gems on a regular basis)

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Why HADOOP for DATA SCIENCE?
Reason #1:
Explore full datasets

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#1: Exploration of Data sets

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Why HADOOP for DATA SCIENCE?
Reason #2:
Mining of larger datasets

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#2: Mining of larger data sets
More Data ---> Better Outcomes

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Why HADOOP for DATA SCIENCE?
Reason #3:
Large-scale data preparation

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#3: Large-Scale Data preparation
80% of data science work is data preparation

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Reason #4:
Accelerate data-driven innovation
Why HADOOP for DATA SCIENCE?

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Speed Barriers of traditional Data Architectures

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“Schema on read” means faster time-to-innovation

26. Demo

27. Questions
Slide 27

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