Data Warehousing
Introduction
Data warehousing is a technology that aggregates structured data from one or more sources so that it can be compared and analyzed for greater business intelligence. Data warehouses are typically used to correlate broad business data to provide greater executive insight into corporate performance.
History of Data Warehousing
The concept of data warehousing dates back to the late 1980s when IBM researchers Barry Devlin and Paul Murphy developed the "business data warehouse". In essence, the initiative was aimed at providing managers with a comprehensive view of the information being processed by their operational applications. The history of data warehousing is linked with that of business intelligence and database management systems.
Data Warehouse Architecture
Data warehouse architecture refers to the design of an organization's data collection and storage framework. It is a key component of effective data collection, storage, and retrieval. The architecture of a data warehouse is made up of several key components, each of which plays a vital role in the organization and management of data.
Types of Data Warehouse Architecture
There are three main types of data warehouse architecture: single-tier, two-tier, and three-tier. The most commonly used type is the three-tier architecture which is comprised of the bottom tier (database server), the middle tier (OLAP server), and the top tier (client layer).
Data Warehouse Models
There are three main types of data warehouse models: the enterprise warehouse, the data mart, and the operational data store.
Enterprise Warehouse
An enterprise warehouse collects all of the information about subjects spanning the entire organization. It provides corporate-wide data integration, usually from one or more operational systems or external information providers, and is cross-functional in scope. It typically contains detailed data as well as summarized data, and can range in size from a few gigabytes to hundreds of gigabytes, terabytes, or beyond.
Data Mart
A data mart is a subset of an enterprise warehouse. It is oriented to a specific business line or team. Unlike the enterprise warehouse, which is cross-functional in nature, the data mart is usually aligned with a specific business function such as finance, sales, or marketing.
Operational Data Store
The operational data store (ODS) is a database designed to integrate data from multiple sources for additional operations on the data. Unlike the data warehouse, which contains static data, the contents of the ODS are updated through the course of business operations.
Data Warehouse Design
Designing a data warehouse requires a methodical approach to ensure it will be used effectively. This includes understanding the business requirements, defining the necessary components, and ensuring the system is scalable and flexible.
Fact Tables and Dimension Tables
Fact tables and dimension tables are used in a star schema, which is the simplest style of data mart schema. The star schema consists of one or more fact tables referencing any number of dimension tables.
Data Warehouse Operations
Data warehouse operations include extract, transform, load (ETL), manage, and retrieve operations.
Extract, Transform, Load (ETL)
ETL is a type of data integration that refers to the process of extracting data from different sources, transforming it to fit operational needs (which can include quality levels), then loading it into the end target database, more specifically, an operational data store, data mart, or data warehouse.
Data Management
Data management involves checking the data for accuracy and consistency, transforming it into a suitable format, and loading it into the data warehouse.
Data Retrieval
Data retrieval is the process of identifying and extracting data from a database, based on a query provided by the user or application.
Benefits of Data Warehousing
Data warehousing can provide a range of benefits for a company. These include improved data quality, increased data consistency, and the ability to make more informed decisions based on the data.
Challenges in Data Warehousing
Despite the many benefits of data warehousing, there can also be challenges. These can include the high cost of implementing a data warehouse, the complexity of data warehouse systems, and the need for skilled personnel to manage and maintain the system.
Future Trends in Data Warehousing
The future of data warehousing is likely to be influenced by developments in big data, real-time analytics, and cloud technology. These trends are likely to shape the way companies store, analyze, and use their data.