This document discusses big data, Hadoop, data science, and why Hadoop is useful for data science. It begins with defining big data and the 3 V's of big data. It then explains what Hadoop is and how it works using HDFS for storage and MapReduce for processing. The document defines what a data product is and provides examples. It defines data science as extracting meaning from data and building data products. Finally, it argues that Hadoop is useful for data science because it allows exploration of full datasets, mining of larger datasets, large-scale data preparation, and can accelerate data-driven innovation by removing speed barriers of traditional architectures.

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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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