by karthik kadam

Question Bank

M - 1

1. Define big data; explain the dimensions/ characteristics of big data.*
2. With example, explain classification of data*
3. Define analytics scalability, horizontal scalability & vertical scalability.*
4. Briefly describe cloud computing with its three fundamental types.*
5. List the features of grid computing. How does it differ from clusters & cloud computing?*
6. Why do we use distributed computing for analytics of large data sets?*
7. How is data architecture layers used for analytics (Fig 1.2).Explain with functions of each layers.
8. Why is data quality important in discovering new knowledge & Decision making? (Data Integrity, Noise, outliers, missing & duplicate values)*
9. Explain in detail, the process of data pre-processing. List data format used during pre-processing.*
10. List the examples of cloud services for exporting data store. (Fig 1.4).
11. What are the traditional systems for data storage? (1.6, 1.1 to 1.6, 1.7)**
12. What are the functions of data integration software? How does application integration along with data integration help in business processes, intelligence & analytics?
13. Briefly describe Big Data storage concepts (1.6.2)*
14. How does a Big Data stack help in analytics tasks?
15. How does a Berkeley Data analytics stack help in analytics tasks?