Big data refers to large volumes of structured and unstructured data that can be analyzed to reveal patterns and trends. It is characterized by 3 Vs - volume, velocity, and variety. Hadoop and associated tools like HDFS, MapReduce, Hive and NoSQL databases are used to handle big data. These tools provide scalability, flexibility and support both structured and unstructured data. Understanding big data analytics provides opportunities in data science and IT jobs and benefits industries like banking, healthcare, manufacturing and more through real-time insights.

1. V
V
BIG DATAV
- Afif Al Mamun

2. Contents
• Big Data
• Three Vs
• Handling Big Data
• Hadoop
• Hive
• NoSQL
• Why Big Data

3. Big Data
Big data is a term that
describes the large
volume of data – both
structured and
unstructured

4. Three Vs
• Volume
• Velocity
• Variety

5. Three Vs
• Volume
 Terabytes/Petabyte
s of Data
 Transactions
 Social Media

6. Three Vs
• Velocity
 Fast rate of data
receiving
 Smart Devices Real
Time Data
 High Velocity of
Data Streams

7. Three Vs
• Variety
 Different Formats
of Data
 Structured/Unstruc
tured/Semi
Structured
 SQL/NoSQL

8. Handling Big data
• Can not be handled by
conventional DBMS
• HDFS
• Map Reduce Algorithm
• Tools
 Apache Hadoop
 Hive
 NoSQL

9. HDFS
• Hadoop Distributed File
System

10. Map Reduce
• A Word Count Example
by Map Reduce
Algorithm

11. Hadoop Distributed
File System
• HDFS Architecture
• Master – Slave
• Namenode
• Datanodes

12. Hive
• Built on Hadoop
• Supports SQL-like
queries
• Used for analytics,
Reports and
Query Processing
• Integrated with JDBC
• Used by Facebook,
Netflix, LinkedIn

13. NoSQL
• Key-value stores
• Document databases
• Graph database
• Flexibility
• Scalability
• Popular NoSQL Databases
• MongoDB
• Oracle NoSQL
• Cassandra DB

14. NoSQL
Hands on MongoDB
A JSON Document to insert
data in a MongoDB Database
named students

15. NoSQL
vs
RDBMS

16. Importance of Big
Data Analytics
• Data Science Perspective
• Business Perspective
• Real time usability
perspective
• Job market perspective

17. Big Data Analytics and
Data Sciences
• Data Scientists are
responsible to deal with Big
Data in business.

18. Data Scientists are responsible
to deal with Big Data in
business.
Business and Big Data
Analytics

19. Real time benefits of
Big Data Analytics
• Banking
• Healthcare
• Energy
• Technology
• Consumer
• Manufacturing

20. Job Opportunities and
Big Data Analytics
The knowledge and
experience of Big Data
analytics can provide you an
edge over others.

21. References
• https://www.sas.com/en_us/insights/big-data/what-is-big-data.html
• https://hadoop.apache.org/docs/r1.2.1/images/hdfsarchitecture.gif
• https://www.oracle.com/big-data/guide/what-is-big-data.html
• https://blog.sqlauthority.com/2013/10/02/big-data-what-is-big-data-3-vs-of-big-data-
volume-velocity-and-variety-day-2-of-21/
• https://bigdataldn.com/big-data-the-3-vs-explained/
• https://www.edureka.co/blog/mapreduce-tutorial/
• https://www.toptal.com/database/the-definitive-guide-to-nosql-databases
• https://www.whizlabs.com/blog/big-data-analytics-importance/