Second Presentation Big Data2222222.pptx

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1. BIG DATA MANAGEMENT

2. CONTENT Introduction What is Big Data Charecteristics of Big Data Types of Data Analytics Process of Data Analytics Tools Used in Big Data Analytics Big Data Sources Big Data Generation Benefits of Big Data Analytics References

3. INTRODUCTION 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 21 st century. The first organization to embrace it where 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.

4. What IS BIG DATA? ‘ 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 analysed with traditional computing techniques.

5. Characteristics of Big Data Three Characteristics of Big Data V3s Volume Data quantity Velocity Data speed Variety Data types

6. Variety  refers to the different types of data that is getting generated. Volume  refers to the amount of data that is getting generated. Volume Velocity Variety Velocity  refers to the speed at which the data is getting generated.

7. Structured Data Unstructured Data Semi-Structured Data Types of Data Analytics

8. Structured Data  refers to the data that has a proper structure associated with it. For example, the data that is present within the databases, the CSV files, and the excel spreadsheets can be referred to as Structured Data.

9. Un-Structured Data  refers to the data that does not have any structure associated with it at all. For example, the image files, the audio files, and the video files can be referred to as Un-Structured Data.

10. Semi-Structured Data  refers to the data that does not have a proper structure associated with it. For example, the data that is present within the emails, the log files, and the word documents can be referred to as Semi-Structured Data.

11. Which is able to help making decisions in easier way Case study and evaluation Identification of particular data Filtering  Data Data extraction Aggregation of Data Visualization of data data analysis Final analysis Result Process of Big Data Analytics

12. MongoDB Hadoop Talend Cassandra Storm Spark Tools Hadoop helps in storing and analyzing big data Tools used in big data analytics MongoDB is used on datasets that change frequently Tools used in big data analytics Talend is a tool used for data integration and management Tools used in big data analytics It is a distributed database that is used for handling chunks of data It is used for real time processing and analyzing large amount of data It is an open source real time computational system Tools Used in Big Data Analytics

13. Big Data Sources Users Application Sensors Systems Large and growing files (Big Data Files)

14. Data Generation Examples Mobile Devices Microphones Readers/Scanners Science facilities Programs/ Software Social Media Cameras

15. Big data analytics is used for risk management Big data analytics is used to improve customer experience Big data analytics is used for product development and innovations Big data analytics helps in quicker and better decision making in organizations  Google has mastered the domain of big data analytics and it has developed several tools and techniques to capture the data of users which includes their preference, their likes, dislikes, the area of specialization, their requirement etc. Benefits of Big Data

16. references www.Wikipedia.com www.slideshare.com www.computereducation.org Books- Big Data by Viktor Mayer-Schonberger THANK YOU!