Data warehouse architecture is the design and building blocks of the modern data warehouse. With the evolution of technology and demands of the data-driven economy, multi-cloud architecture allows for the portability to relocate data and workloads as the business expands, both geographically and among the major cloud vendors such as Amazon and Microsoft.
There are three approaches to constructing a data warehouse:
Bottom Tier
The bottom tier or data warehouse server usually represents a relational database system. Back-end tools are used to cleanse, transform and feed data into this layer.
Middle Tier
The middle tier represents an OLAP server that can be implemented in two ways.
The ROLAP or Relational OLAP model is an extended relational database management system that maps multidimensional data process to standard relational process.
The MOLAP or multidimensional OLAP directly acts on multidimensional data and operations.
Top Tier
This is the front-end client interface that gets data out from the data warehouse. It holds various tools like query tools, analysis tools, reporting tools, and data mining tools.
COMPONENTS OF DATA WAREHOUSE ARCHITECTURE
1. Data Warehouse Database
The essential component of data warehouse, a database stores and provides access to all business data. Cloud-based database services include Amazon Redshift and Azure SQL.
2. Extraction, Transformation, and Loading Tools (ETL)
Traditional ETL tools extract data from various sources, transform it into a digestible format, and load it into a data warehouse.
3. Metadata
Metadata provides a framework for and descriptions of data, enabling the construction, storage, handling, and use of the data.
4. Data Warehouse Access Tools
Access tools allow users to discover actionable information from warehouse data. These warehouse tools can include query and reporting tools, application development tools, data mining tools, and OLAP tools.