Big Data Analytics

1. Arun Kumar
MSc(Computer Science),
Don Bosco College Yelagirihills.
10/3/20181 Don Bosco College, Yelagiri hills.

2. Outline
 Big Data : An Introduction
 Big Data Analytics
 Big Data Analytics : Applications and Business
prosperity
 Big Data Technology
 Big Data : Issues and Challenges
 Conclusion
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3. Big Data:
An Introduction
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4. Introduction
4
 Data
 Facts and piece of information collected together
for reference or analysis
 Information processed or stored by computer &
other electronic devices
 Text, image, audio, video, etc.,
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5. Introduction
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 Big data is similar to data, but it’s not behave the
same
 The term ‘big data’ applies to information that cannot be
processed or handled using traditional processes or tools
1 8 bit
1024
byte
1024
kilobyte
1024
megabyte
1024
Gigabyte
1024
Terabyte
1024
petabyte
1024
Exabyte
1024
zeta byte
Bit
Byte
Kilobyte
Megabyte
Gigabyte
Terabyte
Petabyte
Exabyte
Zetabyte
Yottobye
Don Bosco College, Yelagiri hills.

6. Definition
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 There is no single standard definition.
 Big data is high-volume, high-velocity and high-
variety information assets that demand cost-effective,
innovative forms of information processing for enhanced
insight and decision making.
-Gartner.
 “Big data exceeds the reach of commonly used hardware
environments and software tools to capture, manage,
and process it with in a tolerable elapsed time for its user
population.” -Teradata Magazine article,
2011.
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7. Introduction
 Characteristics of Big Data
Big Data
Velocity
Variety
Volume
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8. Introduction
 Characteristics of Big Data.
 Volume:
 Huge size of data (Tera byte to Peta byte) at rest.
 Velocity:
 Data in motion (streaming data).
 Variety:
 Varieties of data (image, audio, text, video, etc).
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9. Introduction
 Characteristics of Big Data
 Now researchers include more V’s
 Veracity
 Value
 Variability
.
.
.
.
 Victory
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10. Volume
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11. Variety
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12. Velocity
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13. Sources of Big Data
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 What is big data?
 Every day, we create 2.5 quintillion bytes of data
— so much that 90% of the data in the world today has been created
in the last two years alone.
 Data comes from everywhere:
 sensors used to gather climate information
 posts to social media sites
 digital pictures and videos
 purchase transaction records
 cell phone GPS signals, etc.
 This data is big data.
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14. Web & Ecommerce
BECOMES
BIG
DATABank/Credit card
Transactional
Mobile
Social
Video & Preference
Machine & Sensor
Retail POS
Sources of Big Data
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15. Who is generating big data?
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 The Model of Generating/Consuming Data has
Changed
Old Model: Few companies are generating data, all others are consuming
data
New Model: all of us are generating data, and all of us are consuming
data
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16. 10/3/201816 Don Bosco College, Yelagiri hills.

17. What we know or see
What’s actually there
What does Big Data look like ?
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18. Area of Applications
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 Health care / Biotech.
 E – Governance.
 Social Networks /
Social Media.
 Weather Forecasting.
 Education data.
Don Bosco College, Yelagiri hills.

19. Area of Applications
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 Banking / Insurance / Finance.
 Retail industries.
 CRM / Customer Analytics.
 Airways and etc.,.
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20. Big Data Analytics
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21. Definition
 Big data analytics is the process of examining
enormous amounts of data of a variety of types to
uncover hidden patterns, unknown correlations and other
useful information.
 Example:
Searches in “friends” networks at social-networking
sites, involve graphs with hundreds of millions of nodes
and many billions of edges.
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22. Why Big Data Analytics Feasible?
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 Increased storage capacities
 Next generation products
 Cost Reduction
 Faster and better decision making
 Communication networking
 Improved services or products
 Distributed processing technologies

23. Stages in Big Data Analytics
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24. Available Analytic Methods
 Traditional Data Processing systems
 Information Processing using statistical tools
 Knowledge Engineering and Intelligence Systems
 Business Analytics using Data mining
 Business Intelligence
 Genetic Algorithms
 Machine learning algorithms
 Exploratory data analysis and etc.,
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25. Types of Big Data Analytics
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Analytics
Descriptive:
what is
happened?
Predictive:
what will
happen?
Prescriptive:
What
should
happen?
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26. Capture
Organize
IntegrateAnalyze
Act
The Cycle of Big Data Management
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27.  Analysis of data is a process of,
with the goal of discovering useful information,
suggesting conclusions, and supporting decision-making.
Activities in Analytics
 Inspecting
 Cleaning
 Transforming
 modeling
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28. Why new analytical method needed?
 Big in Size – (Volume)
 Unstructured data – (Variety)
 To analyze the streaming data (High-Velocity)
 Distributed
 Need of parallel analytics
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29. Big Data Technology
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30. Key Technologies for Big data
 DFS (Distributed File System):
 Large files are split into parts
 Move file parts into a cluster
 Fault-tolerant through replication across nodes while being rack-
aware
 MapReduce:
Move algorithms close to the data by structuring them for
parallel execution so that each task works on a part of the data. The
power of Simplicity!
 NoSQL:
A NoSQL (often interpreted as Not Only SQL) database
provides a mechanism for storage and retrieval of data that is modeled
in means other than the tabular relations used in relational databases.
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31. Key Technologies for Big data
Three key technologies that can help to handle big data:
 Information management for big data: Manage data as
a strategic, core asset, with ongoing process control
High-performance analytics for big data: Gain rapid
insights from big data and the ability to solve increasingly
complex problems
Flexible deployment options for big data: Choose
between options for on premises or hosted, software-as-a-
service (SaaS) approaches
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32.  Fast Processors and Massively Parallel Processing
(MPP)
 Distributed File System
 Apache Hadoop
 Data Intensive Computing Strategies
 Low cost storages, In-Memory Processing
Technologies for Big data
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33.  Hadoop Distributions
 Hortonworks
 Cloud Operating System
 Cloud Foundry — By VMware
 OpenStack — Worldwide participation and well-known
companies
 Storage
 fusion-io — Not open source, but very supportive of Open
Source projects; Flash-aware applications.
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Technologies for Big data

34.  Python — Awesome programming language.
 Mahout — Machine learning programming
language.
 R — Best among Data mining tools.
 Storm — Stream processing by Twitter.
 Giraph — Graph processing by Facebook.
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Development Platforms and Tools

35.  NoSQL Databases
 MongoDB
 Cassandra
 Hbase (Hadoop)
 SQL Databases
 MySql — Belongs to Oracle
 PostgreSQL — Object Relational Database
 TokuDB — Improves RDBMS performance
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Databases

36. Visualization tools
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 Maps
 Charts (pie, bar, plot, etc)
 Graphs

37. Big Data: Issues &
Challenges
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38. Challenges
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The Bottleneck is…..
 In technology
 New architecture, algorithms, techniques are needed
 Also in technical skills
 Lack of experts in using the new technology
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39. Data sources
Big Data Analytics
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40. Challenges
Internet of Things related
 The amount of data needed to sort, improve, integrate,
analyze and manage is huge.
 Sensor devices, constantly chattering updates about
moisture, light, movement
 Real-time stream data analytics platform that can handle
Big Data and a scalable infrastructure to support it.
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41. Challenges
Cloud computing related
 Traditional WAN-based transport methods cannot move
terabytes of data at the speed dictated by businesses
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42. Classified Issues & Challenges
 Storage
 Management
 Processing
 Visualization
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43. Challenges: Storage related
 Clearly not enough hard disks/devices.
 Distributed storage is still not enough, manufacturers
cannot make enough storage devices in time.
 Speed in writing to devices, bigger data paths/data-bus
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44. Challenges: Management related
 Data Collection
 Organize the varieties of data
 Need of distributed environments
 Need of new analytical methodology
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45. Challenges: Processing related
 Integrating data using Filters
 “What” Data and “How” ?
 Effective Data processing system Design
 Latency and Bandwidth
 Streaming data processing
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46. Challenges: Big data visualization
 Meeting the need for speed
 Understanding the data
 Addressing data quality
 Displaying meaningful results
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47. Conclusion
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48. For Researchers
 Research institutes and companies invite more data
scientists for the research and development.
 Research opportunities in R & D in the respective fields
such as
 Telecom industry
 Retail industry
 Social networks
 Healthcare industry and so on.
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49. For Students
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 Develop deep analytical skills to grab Analyst positions
 Basic knowledge about Optimization techniques, Data
mining, Machine Learning algorithms, etc.
 Keep an eye on evolving technologies

50. Thank you
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