If you’re into business intelligence, analytics, or data-driven decision making, you’ve probably heard of data warehouse. But what is data warehouse architecture, exactly, and should you care? In this blog post, we’ll explain everything you need to know about data warehousing and its architecture. Let’s get started.
A data warehouse is a centralized repository of integrated data from various sources. A data warehouse enables you to store, access, and analyze large amounts of historical and current data, and generate insights that can help you make better business decisions.
A data warehouse architecture is the design and structure of a data warehouse and consists of following components:
There are four common types of data warehouse architecture, depending on the number and location of the data storage layers. Some of the common types are:
Single-tier architecture is the simplest type of data warehouse architecture, where the data is stored and accessed in a single layer, without any intermediate processing or staging. This type is easy to implement and maintain, but it may suffer from poor performance and scalability, as the data warehouse must handle both the data integration and data access tasks.
Two-tier architecture is a type of architecture where the data is stored in two layers, one for data integration and one for data access. The data integration layer performs the ETL process and stores the data in a staging area, while the data access layer stores the data in a data warehouse schema and provides the interface and tools for data analysis. This type improves the performance and scalability of the data warehouse, but it may introduce complexity and redundancy as the data must be moved and stored in two layers.
Three-tier architecture is the most common type of architecture. Here, the data is stored in three layers, one for data integration, one for data storage and one for data access. The data integration layer performs the ETL process and stores the data in a staging area, the data storage layer stores the data in a data warehouse schema and provides the data quality plus security features, and the data access layer provides the interface and tools for data analysis.
This type of architecture optimizes the performance and scalability of the data warehouse but may require more resources and maintenance as the data must be moved and stored in three layers.
Distributed architecture is a type of data warehouse architecture where the data is stored and accessed in multiple locations such as different servers, regions, or clouds, that are connected by a network. The data can be distributed in different ways, such as horizontally, vertically, or hybrid, depending on the data characteristics and requirements. The distributed architecture type enhances the performance and scalability of the data warehouse but may increase the complexity and cost as the data must be synchronized and coordinated across locations.
Follow these best practices to design, develop and maintain a data warehouse architecture that meets your business needs and goals:
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Data warehouse architecture is the design and structure of a data warehouse (a centralized repository of integrated data from various sources).
There are four types of data warehouse architecture; single-tier, two-tier, three-tier and distributed. Each type has its own advantages and disadvantages, and you should choose the one that fits your data characteristics and requirements.
To design, develop and maintain a data warehouse architecture that meets your business needs and goals, you should follow some practices such as choosing the appropriate type and model of architecture, ensuring data quality, consistency, and security, optimizing data warehouse performance, scalability and reliability, and updating and evolving data warehouse architecture to meet changing business needs and challenges. You can also share your requirements with us on Facebook and Instagram.