Big Data Analytics with Spark

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What will you learn today ?
 What is Apache Spark ?
 How Spark fits into Hadoop Ecosystem ?
 Why Spark for Big Data Analytics ?
 Spark’s popularity
 Hands-On : Analyzing data with Spark

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Apache Spark
Apache Spark is a general purpose data processing
engine with in-memory computing
Spark provides API for Scala, Java, Python and R which
makes Spark widely adopted for data processing

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How Spark fits into Hadoop Ecosystem ?
Spark is intended to enhance, not replace, the Hadoop stack
Spark is designed to read and write data to HDFS as well as other storage systems such as
CSV files, Amazon S3 and NoSQL databases

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Why Spark for Big Data Analytics ?
What makes
Spark
suitable for
Big Data
Analytics ?

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Why Spark for Big Data Analytics ?
Following features make Spark, the best fit for Big Data Analytics :
 Spark simplifies data analysis
 Spark provides built-in libraries to do advanced analytics
 Spark speaks more than one language
 Spark provides faster results
 Spark allows you to use different Hadoop vendors

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Word Count Problem - MapReduce
MapReduce Code for a Simple Word Count Problem

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Word Count Problem - Spark
Spark Scala Code for Word Count Problem
Spark Python Code for Word Count Problem
Clearly processing data with Spark is much
easier than MapReduce and Spark gives you
the flexibility to choose your favorite
language Scala, Java, Python etc.

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Spark is blazingly Fast

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Spark Libraries
 Spark SQL : Spark’s module for working with structured data
 MLlib : Spark’s machine learning library
 GraphX : Spark’s API for graph computation
 Spark Streaming : Spark’s API to process streaming data

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Spark Multiple Language Support

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Spark in one Snapshot

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Spark Use Cases
Different companies are using Spark
for solving various problems e.g.
recommendation systems, business
intelligence, fraud detection etc.

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Who is using Spark?
A complete list of companies using Spark can be found here : https://cwiki.apache.org/confluence/display/SPARK/Powered+By+Spark

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Spark is here to stay
Spark is not one of those "here today, gone tomorrow". Spark is here to stay
for the foreseeable future, and it is well worth to get your teeth into it in
order to get some value out of your data

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Hands-on
Analyzing data with Spark

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References
IBM backs Apache Spark for Big Data Analytics :
http://www.forbes.com/sites/paulmiller/2015/06/15/ibm-backs-apache-spark-for-big-data-analytics/
How eBay uses Spark to ignite Data Analytics :
http://www.ebaytechblog.com/2014/05/28/using-spark-to-ignite-data-analytics/
Why Cloudera is saying 'Goodbye, MapReduce' and 'Hello, Spark' :
http://fortune.com/2015/09/09/cloudera-spark-mapreduce/
5 reasons to turn to Spark for Big Data Analytics :
http://www.infoworld.com/article/2897287/big-data/5-reasons-to-turn-to-spark-for-big-data-analytics.html

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