What Are Components Of A Data Warehouse Architecture


Data warehouses are modern architectures of data management and analytics. In order to extract insights from large volumes of data and understand the relationship between entities, businesses rely on data warehouses. A data warehouse architecture consists of several components which together, makes storage and access of data possible and faster.

Data Warehouse Components

Data warehouse architectures are designed to house and analyse vast amounts of data. The major components in a data warehouse architecture are the Data Marts, Data Sources, ETL Process, Metadata, Reporting Platforms, Online Analytical Processing (OLAP) Servers, Data Model and Data Warehouse Database.

Data Marts

Data Marts are a subordinate version of the Data Warehouse and can be managed by using an information dashboard and dashboard tools. Data Marts are used to hold operational data, sales details, and product information. These are helpful for content management and provide the ability to update and store real time data.

Data Sources

Data sources offer the data warehouse the ability to store vast amounts of data from both internal such as CRM, databases and external sources such as social media applications. Data sources are beneficial for storing data and ensure data is used more effectively.

ETL Process

Extract Transform Load (ETL) is a process of which data is collected from different sources, transformed and loaded into the data warehouse. This process is critical in data management and analysis as it extracts and combines data into one data source in an organised manner.


Metadata are ‘data about data’ and describes the elements and organisation of data within the data warehouse. Without tracking these data elements, data analytics would be out of control. Metadata helps track multiple internal and external sources and when combined with data parsing, provides valuable insights for businesses.

Reporting Platforms

Reporting platforms provide end-users access to the data warehouse, allowing them to view data in the suitable format. The platforms fulfil customer demands on time by addressing the exact data at the time of querying. Moreover, these platforms enable faster decision-making as the data is well organised, easy to understand and quickly available.

OLAP Servers

Online analytical processing (OLAP) servers helps to analyse complex data models more quickly and efficiently. OLAP servers are used to discover patterns and trends within very large data sets and makes better decisions out of them. Additionally, they provide a range of powerful analysis facilities, together with flexibility to suit user requirements.

Data Model

Data warehouses use multidimensional models, commonly known as a star schema data model. Using multiple facts and dimensions, this model provides summarised data which can be used more conveniently and quickly. Additionally, this model can be updated and amended depending on the changing customer requirements.

Data Warehouse Database

Data Warehouse databases store data which is combined from multiple sources for an efficient data analysis. Several software solutions are made to aid in data management, such as Oracle and Microsoft SQL servers, as well as open source solutions such as PostgreSQL and MySQL.

Data Integration

Data integration is the process of combining data from multiple sources for effective data management. It is important for businesses to invest in quality data integration techniques to make it easier to fetch data from the different sources. ETL tools, API’s or data mash ups are some of the techniques to assist with data integration.

Data Visualisation

Data visualisation is a key part of data warehousing. It helps to convert complex data into simple visuals so that it can be presented more clearly. Through data visualisation, users can explore patterns, relationships and trends in the data, making it easier to analyse and convey data.

Analytical Tools

Analytical tools are essential to process the data and search for valid insights. Data analysis helps to reveal new market trends and uncover opportunities which would not have been discovered without the use of a data warehouse. Heuristic algorithms can also be used to as part of predictive analysis, either to identify customer behaviour or to forecast future events.

Cloud Solutions

Cloud solutions are gaining attention for their scalability, reliability and cost-effectiveness. Cloud solutions combine the best of on-premise options as well as Cloud-based options, offering much faster insights and improved data storage capabilities. To ensure large companies have quick access to the data they need, they usually depend on cloud solutions.

Data Security

Data security is a top priority within data warehouses. Companies must provide access to the data warehouse to authorised people only and keep data secure from malicious attackers or competitors. Encryption and authentication measures such as token-based and even machine learning algorithms can be used for this.

Final Thoughts

Data warehousing architecture is an essential tool for businesses worldwide for managing and analysing large amounts of data. The complexity of this process is vast and without the use of different components, the process would be time consuming and prone to errors. And therefore, having knowledge of components of a data warehouse architecture is essential.

Anita Johnson is an award-winning author and editor with over 15 years of experience in the fields of architecture, design, and urbanism. She has contributed articles and reviews to a variety of print and online publications on topics related to culture, art, architecture, and design from the late 19th century to the present day. Johnson's deep interest in these topics has informed both her writing and curatorial practice as she seeks to connect readers to the built environment around them.

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