Applications of Hadoop in Social Media

Slide ‹#›© 2015 BlueCamphor Technologies (P) Ltd. www.skillspeed.com
Applications of BIG Data in
Social Media Networks

Introduction to Big Data
Big data is the term for a collection of data sets so
large and complex that it becomes difficult to
process using on-hand database management
tools or traditional data processing applications
Systems / Enterprises generate huge amount of
data from Terabytes to Petabytes
Understanding Big Data can give you innovative
solutions to various queries.

Hadoop in Social Media Networks
Facebook
Twitter
LinkedIn
Pinterest
Instagram
StumbleUpon

Facebook
Facebook is one of Hadoop and Big Data's biggest champion and is rumored to operate the
single largest HDFS cluster in the world comprising on 100 petabytes of disk space. The site
stores more than 250 billion photos, with 350 million new ones uploaded every day.
Hive, the data warehousing infrastructure Facebook helped develop, is central to meeting the
company's reporting needs. Facebook uses Hive for faster querying on graph tools.
Hadoop is used in every Facebook product and in a variety of ways. User actions such as a "like"
or a status update are stored in a highly distributed, customized MySQL database but
applications such as Facebook Messaging run on top of HBase, Hadoop's NoSQL database
framework.
All messages sent on desktop or mobile are stored in HBase. Additionally, the company uses
Hadoop and Hive to generate reports for third-party developers and advertisers who need to
track the success of their applications or campaigns.
Get Started with BIG Data & Hadoop
Applications of Hadoop in Social Media Networks

Twitter
Twitter uses Hadoop, especially Pig and Hbase for Data analysis. Twitter has large data storage
and processing requirements, therefore it uses Hadoop to optimize data storage and workflow
solutions.
It uses Hadoop and Pig layers on top of LZO files which are compressed for quick analysis of
data. Due to this, it has almost completely negated infrastructure failures.

LinkedIn
LinkedIn crunches more than 120 billion relationships per day and uses Hadoop capabilities to
blend large scale data computation with high volume, low latency site serving.
The ‘People You May Know’ feature is the result of scanning through billions of user triggers &
activities via a pipeline of 82 MapReduce jobs each processing 16TB of data. This job uses a
statistical model to predict the probability of two people knowing each other. It uses bloom
filters to speed up large joins, yielding a 10x performance improvement.
LinkedIn also builds an index structure in their Hadoop pipeline - this produces a multi-TB
lookup structure that uses perfect hashing (requiring only 2.5 bits per key). This process results
in faster server - cluster responses; it takes LinkedIn about 90 minutes to build a 900 GB data
store on a 45 node development cluster.

Pinterest
Pinterest’s engineering team has created a self-serving platform — comprised of home grown,
open source and commercial tools — that hooks in with Hadoop to orchestrate and process all
of the company’s enormous amounts of data, consisting of 30 billion pins.
They extensively utilize MapReduce for the processing of stored data which is synced with an
AWS cloud, wherein all their data is currently stored.
Instagram
Instagram operates one of the world’s largest Hadoop clusters and very likely the world’s
largest MySQL implementation. It has developed everything from a PHP-optimized platform to
a NoSQL database to a tool for auto-provisioning and configuring tens of thousands of servers
wherein all the images are stored & processed..

StumbleUpon
StumbleUpon uses Hadoop to make the best recommendations. Every thumb-up, stumble and
share (among other feedback from users) is stored so it can make better decisions about what
pages to show you next.
StumbleUpon uses Hbase extensively because of its cost-effectiveness, fast data retrieval
capabilities and dependability.

© 2015 BlueCamphor Technologies (P) Ltd.
How Are Insights Derived from BIG
Data?
Say Hello to Hadoop Development :)

Introduction to Hadoop
Apache Hadoop is a framework that allows the distributed processing of large data sets across clusters of
commodity computers using a simple programming model.
It is an Open-Source Data Management technology with extensive storage and distributed processing.
Hadoop
Characteristics
Flexible
Reliable
Economical
Scalable Get Started with BIG Data & Hadoop

Why Should You Learn Hadoop?
• Hadoop is a modern BIG Data processing framework which utilizes distributed computing clusters.
• It can process, manage and store unstructured data with absolute ease.
• Due to its scalability & effectiveness, companies are heavily adopting Hadoop.
• It has become the standard for storing, processing and analyzing big data.
• BIG Data & Hadoop professionals are in extremely high demand.
• This is the first step to becoming a data scientist or data architect.

Reports – 2015
Here are some of the other Reports that convey the same message:
• “It is expected that Hadoop and Big Data Analytics will grow to around $13.9 billion from 2012 to 2017.”
(Markets and Markets Research Report)
• “The Data market currently with the fastest growth are Hadoop and NoSQL software and services.”
(Technology Research Organization, Wikibon)
• “The Big Data market will likely hit $23.8 billion with a 31.7% rise per year.” (IDC Report)
• “Almost 90% organizations have embarked on Hadoop related projects and thus Hadoop skills are in huge
demand.” (According to the Big Data Executive Survey 2013)
• “Companies hiring Hadoop professionals Analytics professionals obtain a 250 percent hike in their
salaries.” (According to Analytics Industry Report)
Gartner had predicted, “Hadoop will be in most advanced analytics products by 2015.” By far and large, this
prediction has turned out to be true. Hadoop is “The Technology” to harness Big Data

Predictions for 2015
According to the Forrester Report:
• A new data economy will arise, Hadooponomics. Due to its vast data prowess, enterprise adoption of
Hadoop will become compulsory. All those companies, which were unsure of adopting Hadoop till 2014,
are bound to adopt it in 2015
• The surge in demand for Hadoop job professionals will be fulfilled by Java professionals & other software
engineers who will upgrade their skills
• The creation of SQL-on-Hadoop options will further enhance usability & adoption
• Due to the introduction of Yarn (Version 2.0), the usage of Hadoop will go beyond analytics. It will
transform into an application platform giving birth to new frameworks in the Hadoop ecosystem
• New Hadoop sources will emerge from organizations like Oracle, HP, Tibco and SAP. Vendors such as Red
Hat, VMware and Microsoft will include Hadoop in their operating Systems

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Course Topics
Module 1
Introduction to Big
Data and Hadoop
Module 2
HDFS Internals, Hadoop
Configurations and
Data Loading
Module 3
Introduction to Map
Reduce
Module 4
Advanced Map Reduce
Concepts
Module 5
Introduction to Pig
Module 6
Advanced Pig and
Introduction to Hive
Module 7
Advanced Hive
Concepts
Module 8
Extending Hive and
HBase Introduction
Module 9
Advanced HBase and
Oozie Introduction
Module 10
Project Set-up
Discussion

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Applications of Hadoop in Social Media

Applications of Hadoop in Social Media

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