1.
Name of the Book:Understanding Big Data: Analytics
for Enterprise Class Hadoop and streaming Data
Author:Paul C. Zikopoulos, Chris Eaton, Dirk deRoos,
Thomas Deutsch, George Lapis and Steven Sit.
Date:

2.
Introduction
• Data is everywhere. In fact, the amount of digital data
that exists is growing at a rapid rate, doubling every year.
some researchers states that Data is growing faster than
ever before and by the year 2020, about 1.7 megabytes
of new information will be created every second for
every human being on the planet. Which makes it
extremely important to at least know the basics of the
field of big data and data analytic and knowing the
difference between the two terms.
 source:
Big Data: 20 Mind-Boggling Facts Everyone Must Read,
Forbes magazine

3.
Plan
1. What Is Big Data?
2. Why Is Big Data Important?
3. What Is Data Analytic?
4. Where Is It Used ?

4.
What IsBigData?
• the term Big Data applies to information that
can’t be processed or analyzed using
traditional processes or tools.

5.
WhyIs Big Data Important?
Key Big Data Principles
Big Data solutions are ideal:
1. For analyzing not only raw structured data, but
semistructured and unstructured data from a wide
variety of sources.
2. When all, or most, of the data needs to be analyzed
versus a sample of the data
3. For iterative and exploratory analysis when business
measures on data are not predetermined.
4. Aalysis and achieving synergy with existing solutions
for better business outcomes.

6.
Data Analytics involves automating
insight into a certain dataset
Big Data is something that
can be used to analyze
insights wich can lead to
better decision and stratigic
busness move

7.
What Is DataAnalytic?
Analytics Is in Our Blood:
o I need to go to campus faster!
o Hmm.. Looking at the sky today, I think it’ll be
rain

8.
What Is DataAnalytic?
Data analytics (DA) is the process of Examining,
Inspecting, Cleansing, Transforming, and Modeling
data s in order to draw conclusions about the
information they contain, increasingly with the aid of
specialized systems and software like “SAP Analytics”,
“Google Analtics”, “Microsoft power BI”…

9.
Analytics Lifecycle
Explore
Elaborate
Model
Plan Model
Use Prepare Data
Comunicate
•Explore the avaible data
•Formulate the questions
•Determine the
necessary Data
•Collect Data
•Clean Data
•Formulate the
questions
•Determine wich
variables to explain &
wich one sto predict
•Select the possible
model and algorithmes
•Implement the model
•Validate it after
choosing the best one
•Interpret result
•Make decision based
on results
•Goals to reach
 Source:
Schmarzo(2012):Data analytic lifecycle

10.
Data Analytics Domains

11.
Unsupervised analytics :when we
don’t know the truth about
something in the past. The
result is segment that we need
to interpret
Example:
We want to do segmentation over
the student, based on the
historical exam score,
attendance, and late history.
Supervised analytics: when we
know the truth about
something in the past
Example:
we have historical weather
data. The temperature,
humidity, cloud density and
weather type (rain, cloudy, or
sunny). Then we can predict
today weather based on temp,
humidity, and cloud density
today
TypesOf Analysis
Do we need only descriptive analysis? Or we need to go with predictive analysis?
Predictive Method

12.
TypesOf Analysis
Do we need only descriptive analysis? Or we need to go with predictive analysis?
descriptive analysis
does exactly what its
name implies to
"describe" or
summarize the raw
data and make them
interpretable by
humans.
Descriptive Method

13.
TypesOf Analysis
Do we need only descriptive analysis? Or we need to go with predictive analysis?
Examines data or content to
answer the question “Why did it
happen?”, and is characterized
by techniques such as data
discovery and data mining.
Diagnostic Method

14.
Conclusion
Big Data Analytics is a hot research topic among the database
researchers as well as the business community. However,
currently we have different methods to analyse big data
which we have mentioned in our paper but there is a lot of
scope to create or invent new method of analytics. There
are different tools and open source software available.
Some of which we have mentioned briefly in the paper.
There is a scope for the future research to compare the
tools and find out the best in a particular situation by
applying it. Also new can always be searched and invented.
There are many more issues which can be further
investigated like: Big data privacy and security,
completeness, Data Quality etc

15.
References