Big data is large amounts of data that are difficult to manage and process using traditional database tools. It is characterized by high volume, velocity, and variety. Big data is now commonly found in data warehouses, online transaction processing systems, social networks, and scientific devices. To effectively filter big data, it must be extracted from raw feeds, transformed into a usable set of data, and loaded into systems. While big data enables better decision making and new opportunities, it also poses risks like being overwhelmed by the data or privacy issues that require mitigation through the right expertise, focus, and regulations.

1. Big Data
INTRODUCTION:
“Big Data” has become an extremely popular term, due to the well-documented explosion in the
amount of data being stored and processed by today’s businesses. Big data is about more than
just the “bigness” of the data. It may be defined as:
“Big Data is too large amount of data to manage using typical database software tools.”
2012 - 2.7 Zettabyte (10^21)
2020 - 35 Zettabyte (10^21)
Per day
Twitter- 7 TB
Facebook- 10 TB
FACTORS:
Volume
Value BigData Velocity
Variety

2. WHERE SEEN:
Data warehouses
OLTP(Online Transaction Processing)
Social networks
Scientific devices
FILTERING BIGDATA EFFECTIVELY:
EXTRACT
Raw feed of data
TRANSFORM
Usable set of data
LOAD
Usable data loading
 Focus on important pieces of data.

3. WHY BIGDATA:
Taking better decisions.
Decide Acquire
Organize
Analyze
(Distill)
Product arrangement
 Locating the dead zone.
Generates financial values across sectors.
RISKS:
Will be so overwhelmed.
Costing.
Privacy.
RISK MITIGATION:
Need the right people & solve the right problems.
It isn’t necessary to handle 100% data all the time.
Formulate legal regulations.
CONCLUSION:
Banking industry was very hard to handle even a decade ago. But there are thousands of banks
now. So, the volume of “Big” will change but Big Data will continue to evolve.