1. Much ambiguity exists in defining Big Data.

2. For a data set to be considered Big Data, it must display all the “3 Vs” – volume, velocity and variety.

3. Scaling out is keeping the same number of systems, but migrating each system to a larger one.

4. In many ways, the issues of associated with volume and velocity are the same.

5. The analysis of data to produce actionable results is feedback loop processing.

6. Relational databases rely on unstructured data.

7. One tenet of Big Data is that all data that is capable of being captured should be.

8. The ability to graphically data in a way that makes it understandable is the concept of value.

9. Characteristics that are important in working with data in the relational database model also apply to Big Data.

10. Hadoop is a database that has become the de facto standard for most Big Data storage and processing.

11. Under the HDFS system, using a write-one, ready-many model simplifies concurrency issues.

12. A block report is used to let the name node know that the data mode is still available.

13. A reduce function takes a collection of key-value pairs with the same key value and summarizes them into a single result.

14. Hive is a good choice for jobs that require a small subset of data to be returned very quickly.

15. Hadoop is a high-level tool that requires little effort to create, manage and use.