SQL SERVER – Data Warehousing Interview Questions and Answers – Introduction

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This series is in response to many of my reader’s continuous request to start Data Warehousing Interview Questions and Answers series. This series is written in the same spirit as previous two series which has received good response.

Samples Question from Interview Questions and Answer Series
What is Data Warehousing?
A data warehouse is the main repository of an organization’s historical data, its corporate memory. It contains the raw material for management’s decision support system. The critical factor leading to the use of a data warehouse is that a data analyst can perform complex queries and analysis, such as data mining, on the information without slowing down the operational systems (Ref:Wikipedia). Data warehousing collection of data designed to support management decision making. Data warehouses contain a wide variety of data that present a coherent picture of business conditions at a single point in time. It is a repository of integrated information, available for queries and analysis.

I will post complete series details here. Please watch this space for additional information.

Complete Series of SQL Server Interview Questions and Answers
SQL SERVER – Data Warehousing Interview Questions and Answers – Introduction
SQL SERVER – Data Warehousing Interview Questions and Answers – Part 1
SQL SERVER – Data Warehousing Interview Questions and Answers – Part 2
SQL SERVER – Data Warehousing Interview Questions and Answers – Part 3
SQL SERVER – Data Warehousing Interview Questions and Answers Complete List Download

Additional Series List Available to Download
SQL Server Interview Questions and Answers Complete List Download
SQL SERVER Database Coding Standards and Guidelines Complete List Download

How to Prepare for Data Warehousing Interview Questions

Most interviews in this area circle around a small group of ideas, so learn them well before you memorize answers:

• OLTP versus OLAP, and why a warehouse is kept apart from the systems that run daily business.
• Fact tables and dimension tables, and the difference between a star schema and a snowflake schema.
• The grain of a fact table, which means what one row stands for.
• Surrogate keys and slowly changing dimensions, especially Type 1 and Type 2.

For each idea, prepare one short example from a project you know. A candidate who can explain a star schema with a real sales table is remembered far longer than one who repeats a definition.

Published by Pinal Dave on SQLAuthority. More of my work at pinaldave.com.