The document is a presentation on big data analytics discussing its definition, significance, and the tools and methods necessary for data analysis. It highlights the need for big data analytics in understanding and extracting meaningful information from large, diverse data sets, emphasizing its role in enhancing decision-making and revealing hidden patterns. The conclusion emphasizes the necessity for organizations to adapt to new data management technologies to leverage big data for strategic advantages.

1. A presentation on
BIG DATA ANALYTICS
Submitted By-:
RAVI KANT SHARMA
Submitted To-:
Mr. Sudeep Varshney
Department of Computer Science
School of Engineering and technology

2. KEYWORDS:
 INTRODUCTION
 WHAT IS BIG DATA?
 CHARACTERIZATION OF BIG DATA
 WHAT IS BIG DATAANALYTICS?
 WHY BIG DATA?
 TOOLS USED IN BIG DATA
 NEED OF BIG DATAANALYTICS
 CONCLUSION

3. INTRODUCTION
Imagine a world without data storage a place where every detail about a person or organization,
every transaction performed, Organizations would thus lose the ability to extract valuable
information and knowledge, perform detailed analyses. Now think of the extent of data and
information provided by internet with the increase in storage capabilities and methods of
data collection, huge amounts of data have become easily available.
The size, variety, and rapid change of such data require a new type of big data analytics, as well
as different storage and analysis methods. Such sheer amounts of big data need to be properly
analyzed, and pertaining information should be extracted. The use of advanced analytic
techniques against very large, diverse data sets that include different types such as
structured/unstructured and streaming/batch, and different sizes from terabytes to zettabytes.

4. What is big-data?
‘Big- data’ is similar to ‘Small data’ ,
but bigger in Amount/Size/Volume
But having data bigger consequently
requires different approaches
 Techniques, Tools and Architectures
To solve
 New problems…
 And old problems in an easy and better
way

5. Volume refers to the amount of data
Variety refers to the number of types of data
Velocity refers to the speed of data processing

6. What is Big-Data Analytics?
Big data analytics is the process of examining
Large data sets
Market trends
Relationships
Unknown correlations
Uncover hidden patterns
Customer preferences and
Other useful business information.

7. Why Big-Data?
Key enablers for the growth of Big-Data are :
Increase of storage capacities
Increase of processing power
Availability of data
To make future Prediction
Enhancement in decision making
To provide new opportunities and services

9. Need of big data analytics
People don’t just want to collect data, they want to understand the
meaning and importance of the data and aid them in making decisions.
 Social Media analysis The content generated from social media websites is
enormous and remains largely unexploited we use big data analytics tools and
methods to handle such a big amount of data.
Text mining is used to analyze a document or set of documents in order to
understand the content within and the meaning of the information contained.
Sentiment analysis, or opinion mining, is important as online opinion data,
such as blogs, product reviews, forums, and social data from social media is
growing. Sentiment analysis focuses on analyzing and understanding emotions
from subjective text patterns, and is enabled through text mining.

10. Conclusions
 Big-Data highlights new data management and data analysis technologies
that enable organizations to analyze certain types of data and workloads that
were not previously possible.
 The actual technologies used will depend on the volume of data, the variety
of data, the complexity of the analytical processing workloads involved, and the
responsiveness required by the business.
 Big-Data involves more than simply implementing techniques to understand
the benefits of smarter and more timely decision making.
 It also requires business users to make pragmatic decisions about agility
requirements for analyzing data and producing analytics.

11. THANKS