7-3 Data Warehousing Explained

Key Concepts

• Data Warehousing
• ETL Process
• OLAP
• Star Schema
• Fact Tables
• Dimension Tables
• Data Marts

Data Warehousing

Data Warehousing involves creating a centralized repository of integrated data from various sources. It is designed to support business intelligence activities, such as reporting and data analysis.

Example: A multinational corporation might use a data warehouse to consolidate sales data from different regions and departments. This allows for comprehensive analysis and reporting across the entire organization.

Analogy: Think of a data warehouse as a central archive where all important documents from different departments are stored and organized for easy access and analysis.

ETL Process

ETL stands for Extract, Transform, and Load. It is the process of extracting data from various sources, transforming it to fit operational needs, and loading it into a data warehouse.

Example: A retail company might extract sales data from multiple stores, transform it to standardize formats and remove duplicates, and then load it into a centralized data warehouse for analysis.

Analogy: Think of ETL as a production line where raw materials (data) are extracted, processed (transformed), and assembled into a finished product (loaded into the data warehouse).

OLAP

OLAP (Online Analytical Processing) is a technology used to analyze multidimensional data from different perspectives. It is designed to support complex queries and data analysis in data warehouses.

Example: A financial analyst might use OLAP to analyze sales data by region, product category, and time period to identify trends and make strategic decisions.

Analogy: Think of OLAP as a multi-faceted diamond. Just as a diamond can be viewed from different angles to reveal different aspects, OLAP allows data to be analyzed from multiple perspectives.

Star Schema

Star Schema is a traditional database schema with a central table (fact table) surrounded by dimension tables. It is commonly used in data warehousing to simplify queries and improve performance.

Example: In a sales database, the fact table might contain sales data, while dimension tables contain information about products, customers, and time periods.

Analogy: Think of a star schema as a star with rays extending from its center. The central star (fact table) represents the core data, while the rays (dimension tables) provide additional context and details.

Fact Tables

Fact Tables contain quantitative data and are the central tables in a star schema. They are used to store measures or metrics that can be analyzed, such as sales amounts, quantities, and counts.

Example: In a sales database, the fact table might contain records of individual sales transactions, including the product sold, the customer, the date, and the amount.

Analogy: Think of fact tables as the main stage in a theater. Just as the stage is the central focus of a performance, fact tables are the central focus of data analysis.

Dimension Tables

Dimension Tables contain descriptive attributes that provide context to the data in fact tables. They are used to categorize and filter data for analysis.

Example: In a sales database, dimension tables might include tables for products (with attributes like name, category, and price), customers (with attributes like name, address, and loyalty status), and time (with attributes like date, month, and year).

Analogy: Think of dimension tables as the supporting actors in a play. Just as supporting actors provide context and depth to the main story, dimension tables provide context and depth to the data in fact tables.

Data Marts

Data Marts are subsets of a data warehouse that focus on a specific business area or department. They provide a more focused and manageable environment for data analysis.

Example: A company might have a data mart for the sales department that contains only sales-related data, making it easier for sales analysts to perform their tasks without dealing with irrelevant data.

Analogy: Think of data marts as specialized libraries within a large library. Just as a specialized library focuses on a specific subject, a data mart focuses on a specific business area.