We are to discuss these Datawarehouse terms regulary being asked in Data Related Job Interviews :-1. Data Warehouse 2. Data Mart 3.OLTP, OLAP and their differences 4.Fact and Dimension Tables 6. Difference between fact and Dimension tables 7.Star and Snowflake Schema 8. Difference between Star and Snowflake Schema .

Here’s a detailed explanation of each concept with real-life examples:

1. Data Warehouse

A data warehouse is a centralized repository that stores data from various sources in a single location. It’s designed to support business intelligence activities, such as reporting, analysis, and data mining.

Example: A retail company has data from various sources like sales, customer feedback, and inventory. They create a data warehouse to consolidate this data and analyze sales trends, customer behavior, and inventory levels.

2. Data Mart

A data mart is a subset of a data warehouse that contains data specific to a particular business unit or department. It’s designed to meet the specific needs of a particular group.

Example: The retail company creates a data mart for the sales team to analyze sales data, and another data mart for the marketing team to analyze customer feedback.

3. OLTP, OLAP, and their differences

OLTP (Online Transactional Processing) systems are designed for transactional processing, such as storing and processing sales data. OLAP (Online Analytical Processing) systems are designed for analytical processing, such as analyzing sales trends.

Key differences:

• OLTP: Focuses on transactional data, supports frequent updates, and has fast query performance.
• OLAP: Focuses on analytical data, supports complex queries, and has fast query performance.

Example: A retail company uses OLTP to process sales transactions, and OLAP to analyze sales trends.

4. Fact and Dimension Tables

Fact tables store quantitative data (measures), while dimension tables store qualitative data (attributes).

Example: In a sales fact table, columns like Sales Amount and Quantity are measures, while columns like Date, Product, and Region are attributes stored in dimension tables.

6. Difference between Fact and Dimension Tables

Fact tables:

• Store quantitative data
• Have a large number of rows
• Typically have a composite primary key

Dimension tables:

• Store qualitative data
• Have a smaller number of rows
• Typically have a single primary key

Example: In a sales fact table, Sales Amount is a quantitative measure, while Product Name is a qualitative attribute stored in a dimension table.

7. Star and Snowflake Schema

Star schema: A fact table surrounded by dimension tables, with each dimension table connected directly to the fact table.

Snowflake schema: A fact table surrounded by dimension tables, with each dimension table connected to the fact table through a series of intermediate tables.

Example: A retail company uses a star schema to connect sales fact table to product, date, and region dimension tables. They use a snowflake schema to connect sales fact table to product dimension table, which is connected to subcategory and category tables.

8. Difference between Star and Snowflake Schema

Star schema:

• Easier to understand and query
• Faster query performance
• Less normalized

Snowflake schema:

• More normalized
• Better data integrity
• Slower query performance

Example: The retail company uses a star schema for sales analysis, but switches to a snowflake schema for more complex analysis involving product subcategories and categories.

These concepts form the foundation of data warehousing and business intelligence. Understanding them will help you design and implement effective data warehousing solutions.