DATA WAREHOUSING — Summer 2018

Differentiate between Primitive data and derived data.

What do you mean by data granularity? Explain the concerns of granularity in a data warehouse.

What is multidimensional model? Explain the star and snowflake structures in detail.

Write a short note on partitioning of data in a data warehouse.

What is data warehouse? Explain the need for building a data warehouse.

Explain the issues related to placement of operational data into the data warehouse.

Explain in brief the feedback loop techniques for deciding the levels of granularity in a data warehouse.

What are profile records? Explain the concept of creating multiple profile records along with appropriate example.

Explain the concepts of variable-length data, lock management, index-only processing and fast restore in the context of data warehouse.

What is EIS? State the uses of EIS.

Answer the following : i. Differentiate between structured and unstructured data. ii. Explain the terms “stop word” and “stem” along with appropriate example.

Explain the relation between EIS and data warehouse in detail.

Explain the process of relating the structured and unstructured environments of data warehouse.

Explain the terms “Exploration Warehouse” and “Data Mining Warehouse” in detail.

Answer the following: i. What is ETL? State the varieties of ETL software. ii. Differentiate between ETL and ELT.

What do you mean by independent data mart? State its advantages and disadvantages.

Write a short note on midlevel data model.