Test Your Knowledge on Big Data

Study Notes

Storing and processing Big Data involves analyzing data characteristics, eliminating redundancies, and using NoSQL.
Big Data stores include key value, graph, document, and column-family data models, as well as HDFS, Hbase, and Hive.
Processing Big Data involves integrating disparate data stores, mapping data to programming frameworks, and employing Hadoop MapReduce.
Reasons for the growth of Big Data include increased storage capacity, processing power, and availability of data.
Big Data is different because it is automatically generated by machines, typically a new source of data, not designed to be user-friendly, and may not have much value.
Big Data sources include users, applications, systems, and sensors.
2.5 quintillion bytes of data are created every day, with 90% of the world's data created in the last two years.
Real-time applications such as Facebook and Twitter generate massive amounts of data daily.
IBM claims that 90% of today's stored data was generated in the last two years.
It is important to focus on the important parts of Big Data sources.