Database develop. life cycle - Data Warehousing and ETL Process

A data warehouse is a centralized repository used for storing large volumes of historical data collected from multiple sources. It is optimized for analysis, reporting, and decision-making, rather than daily transaction processing.

Characteristics of Data Warehouse

Data warehouses are subject-oriented, meaning data is organized by business areas like sales or finance. They are integrated, combining data from different sources into a consistent format. Data warehouses are time-variant, storing historical data, and non-volatile, meaning data is not frequently updated.

ETL Process

ETL stands for Extract, Transform, and Load, which is the core process of data warehousing.

Extract

Data is collected from various sources such as databases, files, and applications. These sources may use different formats and structures.

Transform

During transformation, data is cleaned, formatted, and validated. Errors are removed, duplicates are handled, and data is standardized to maintain consistency.

Load

The transformed data is loaded into the data warehouse. This process may occur periodically or in real-time depending on business needs.

Types of Data Warehouses

• Enterprise Data Warehouse (EDW) – centralized data for entire organization

• Data Mart – smaller, department-specific warehouse

• Virtual Data Warehouse – logical views of data without physical storage

Benefits of Data Warehousing

Data warehousing supports business intelligence, improves reporting accuracy, enables trend analysis, and helps organizations make informed decisions.

Challenges in Data Warehousing

Challenges include high implementation cost, data quality issues, performance management, and maintaining data security.