Enterprise Data Warehouses (EDWs) are complex systems composed of various components, each serving a crucial function in managing and utilizing data effectively.
Here’s a breakdown of these essential components and their roles within the enterprise data warehouse ecosystem:
1. Data Sources:
Data sources encompass all repositories where raw data originates or is stored. These sources can vary widely, from simple spreadsheets to sophisticated IoT systems and relational SQL databases.
2. Ingestion Layer:
The ingestion layer is responsible for extracting and delivering data from various sources to the data warehouse. Two primary approaches exist: Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT). ETL tools conduct data extraction, transformation, and loading in a staging area before entering the EDW, while ELT performs transformations within the warehouse itself, bypassing the staging area.
3. Staging Area (Optional):
In the case of ETL, the staging area acts as an intermediate step where data undergoes cleansing, deduplication, merging, and conversion into a standardized format that aligns with the data warehouse’s schema. It may also include tools for data quality management.
4. Storage Layer:
The storage layer is where the data is ultimately housed. With the ELT approach, some additional transformations may occur here, but it’s primarily where data is loaded into its final structure. Data warehouses typically consist of relational databases, often with a database management system and storage for metadata.
5. Metadata Module:
Metadata, or data about data, is stored in a dedicated module within the EDW. Metadata provides essential context, including technical information about data sources and business-related details such as sales regions. A metadata manager oversees this component and may involve additional layers for curating metadata, like data virtualization or data fabric layers.
6. Presentation Layer:
The presentation layer serves as the interface between end-users and the EDW. Often referred to as the Business Intelligence (BI) interface, it provides tools for data visualization, business reporting, and data extraction for tasks like machine learning. This layer empowers users to access and interpret data effectively.