Data analytics is needed everywhere to predict relevant outcomes, increase profits, and efficiently use resources. It involves transforming processed information into knowledge. Companies use data analytics to understand customer behavior and target them with personalized advertisements and offers. Different types of data include structured, semi-structured, unstructured, and big data. Big data is high volume, high velocity, and highly variable information that requires novel processing techniques. Hadoop is an open source software framework that allows distributed processing of big data across clusters of computers using simple programming models like MapReduce. Data analytics helps analyze these different data types to provide business insights.

2. What is Data Analytics ?
Why do we need it ?
Where do we need it ?
We need It to predict something relevant to our goal, increase
profit and for efficient utilization of our resources
We need it everywhere:
Daily Life: Before buying the grocery, Before filling the fuel
Business : To increase Sales
Computer System : Various algorithms ( LRU, MRU ,
Command Queuing Algorithms)
It is a process of transforming processed Information into knowledge

3. Have U ever noticed that the product you select or viewed from ,
AMAZON, SNAPDEAL, MYANTRA.
The Advertisement of the same product is reflect on all the websites and webpages over
which U go?

4. How Dominos is giving Ua offer a PIZZA free on
Thrusday and may be giving
U a GARLIC BREAD with CHEESY DIP
absolutely free?
WHY Supermarket stores are having
SALES and how they are giving a product free?
IF U HAVE SUCH QUESTIONS IN YOUR MINDTHEN
DIVE IN OCEAN OF ANALYSIS

5. DATAANALYTICS (Play with data)

6. ANALYSIS needs DATA
So needs to understand various types of DATA over
which companies work
1. STRUCTURED DATA
2. SEMI STRUCTURED DATA
3. UNSTRUCTURED
BIG DATA

7. Structured Data
 It concerns all data which can be stored in database SQL in
table with rows and columns.
 They have relational key and can be easily mapped into pre-
designed fields.
 Today, those data are the most processed in development and
the simplest way to manage information.
 But structured data represent only 5 to 10% of all
informatics data.

8. Semi Structured Data
• Semi-structured data is information that doesn’t reside in a
relational database but that does have some organizational
properties that make it easier to analyze.
• With some process you can store them in relation database (it
could be very hard for some kind of semi structured data), but
the semi structure exist to ease space, clarity or compute…
Examples of semi-structured : CSV , XML documents are semi
structured documents.
But as Structured data, semi structured data represents a few
parts of data (5 to 10%).

9. Unstructured data
• Unstructured data represent around 80% of data.
• It often include text and multimedia content.
• Examples include e-mail messages, word processing
documents, videos, photos, audio files, presentations,
webpages and many other kinds of business documents.
• Unstructured data is everywhere.
• In fact, most individuals and organizations conduct their lives
around unstructured data.
• Just as with structured data, unstructured data is either
machine generated or human generated.

10. Unstructured data
Here are some examples of machine-generated unstructured data:
• Satellite images: This includes weather data or the data that the government captures in
its satellite surveillance imagery. Just think about Google Earth, and you get the picture.
• Scientific data: This includes seismic imagery, atmospheric data, and high energy
physics.
• Photographs and video: This includes security, surveillance, and traffic video.
• Radar or sonar data: This includes vehicular, meteorological, and oceanographic
seismic profiles.
• The following list shows a few examples of human-generated unstructured data:
• Social media data: This data is generated from the social media platforms such as
YouTube, Facebook, Twitter, LinkedIn, and Flickr.
• Mobile data: This includes data such as text messages and location information.
• website content: This comes from any site delivering unstructured content, like
YouTube, Flickr, or Instagram.

11. BIG DATA

12. • Big Data may well be the Next Big Thing in the IT world.
• Big data burst upon the scene in the first decade of the 21st
century.
• The first organizations to embrace it were online and startup
firms. Firms like Google, eBay, LinkedIn, and Facebook were
built around big data from the beginning.
• Like many new information technologies, big data can bring
about dramatic cost reductions, substantial improvements in the
time required to perform a computing task, or new product and
service offerings.
BIG DATA

13. • ‘Big Data’ is similar to ‘small data’, but bigger in size
• but having data bigger it requires different approaches:
– Techniques, tools and architecture
• An aim to solve new problems or old problems in a better way
• Big Data generates value from the storage and processing of very
large quantities of digital information that cannot be analyzed
with traditional computing techniques.
What is BIG DATA?

14. What is BIG DATA
• Walmart handles more than 1 million customer transactions every
hour.
• Facebook handles 40 billion photos from its user base.
• Decoding the human genome originally took 10years to process; now
it can be achieved in one week.

15. 1st Character of Big Data
Volume
•A typical PC might have had 10 gigabytes of storage in 2000.
•Today, Facebook ingests 500 terabytes of new data every day.
•Boeing 737 will generate 240 terabytes of flight data during a single
flight across the US.
• The smart phones, the data they create and consume; sensors
embedded into everyday objects will soon result in billions of new,
constantly-updated data feeds containing environmental, location,
and other information, including video.

16. 2nd Character of Big Data
Velocity
• Clickstreams and ad impressions capture user behavior at
millions of events per second
• high-frequency stock trading algorithms reflect market
changes within microseconds
• machine to machine processes exchange data between billions
of devices
• infrastructure and sensors generate massive log data in real-
time
• on-line gaming systems support millions of concurrent users,
each producing multiple inputs per second.

17. 3rd Character of Big Data
Variety
• Big Data isn't just numbers, dates, and strings. Big Data is also
geospatial data, 3D data, audio and video, and unstructured
text, including log files and social media.
• Traditional database systems were designed to address smaller
volumes of structured data, fewer updates or a predictable,
consistent data structure.
• Big Data analysis includes different types of data

18. Storing Big Data
Analyzing your data characteristics
• Selecting data sources for ANALYSIS
• Eliminating redundant data
• Establishing the role of NoSQL*
Overview of Big Data stores
• Data models: key value, graph, document,
column-family
• Hadoop* Distributed File System
• HBase
• Hive

19. HADOOP
• Hadoop is not a type of database, but rather a software ecosystem that
allows for massively parallel computing.
• It is an enabler of certain types NoSQL distributed databases (such as
HBase), which can allow for data to be spread across thousands of
servers with little reduction in performance.
• Hadoop is an Apache Software Foundation project that importantly
provides two things:
A distributed filesystem called HDFS (Hadoop Distributed File System)
A framework and API for building and running MapReduce jobs
HDFS is structured similarly to a regular Unix filesystem except that
data storage is distributed across several machines.

20. HADOOP

21. HADOOP
• Google solved this problem using an algorithm called MapReduce.
• This algorithm divides the task into small parts and assigns those
parts to many computers connected over the network, and collects
the results to form the final result dataset.
• Doug Cutting, Mike Cafarella and team took the solution provided
by Google and started an Open Source Project called HADOOP in
2005 and Daug named it after his son's toy elephant.
• Hadoop is an Apache open source framework written in java that
allows distributed processing of large datasets across clusters of
computers using simple programming models.
• Hadoop is designed to scale up from single server to thousands of
machines, each offering local computation and storage.

22. SO TILL NOW WE HAVE STUDIED REGARDING
VARIOUS TYPES OF DATA
Structured Data –> Maintained by RDBMS software
Unstructured Data –> Managed by NOSQL databases
BIG Data –> Handled by HADOOP (MAP REDUCE FRAMEWORK)
BUT to INCREASE company/organisation PROFIT then after the
management of these data, they needs to be HIGHLYANALYZED.