Data Warehouse Architecture: Types, Components & Concepts

Data Warehouse Architecture: Types, Components, & Concepts. A data warehouse is a large, centralized data repository that combines information from various sources to support business intelligence (BI) and reporting. It facilitates querying, data analysis rather than transaction processing. Unlike operational databases, a data warehouse supports historical data analysis across multiple dimensions, providing a 360-degree view of the business at different points in time.

Data warehouses usually follow a schema design, such as a star or snowflake schema, that groups data into fact and dimension tables. They enable effective decision making processes by providing a unified, coherent picture of business operations.

 Shall we start with article Data Warehouse Architecture: Types, Components & Concepts.

Core Components of Data Warehouse

A typical data warehouse has various components. Most data warehouses are built around a relational database, that are on-premise or on cloud platforms. Here are the main components of data warehouse:

Source Systems

Source systems in a data warehouse are different platforms or databases where data comes from. Ideally, anything from transactional databases like SQL Server, Oracle, or MySQL, to CRM systems like Salesforce, to ERP systems like SAP or Oracle Financials, and even flat files, Excel files, or web services. These are basically the data sources that provide data for the warehouse.

Elastic, Transform, and Load (ETL)

ETL tools are the central components of the data warehouse. Basically, this is the data staging area where data from source systems is extracted and prepared for loading into the data warehouse. ETL processes include cleaning the data (removing duplicates, fixing inconsistencies), transforming it into a format suitable for the data warehouse, and sometimes aggregating it. The staging area is also where data reconciliation and validation happens. Also, this is where you make any necessary changes to ensure it meets the requirements of the data warehouse.

Data Storage

Data storage in the context of a data warehouse refers to the location where the processed and transformed data is stored for easy access and analysis. Usually a relational database management system (RDBMS) that’s been optimized for read access and large scale data analytics, although modern data warehouses also use columnar databases or even cloud storage. Also, the data storage component is where data is organized into fact and dimension tables, following either a star schema, a snowflake schema, or other data models, depending on the data warehouse design.


Metadata explains the warehouse data in depth. It gives data descriptions that make it searchable. Include elements like location, authors, dates, file size, etc. In essence, metadata enables you to organize data in such a way that makes it usable. Essentially, comes in handy during extraction and loading processes. Also simplifies querying and defines how data can be changed or processed.

End User Tools

The end users of a data warehouse are typically business people who need to make sense of vast amounts of data. Either business analysts looking for trends in data, data scientists building predictive models, or executives tracking key performance metrics.Some most commonly used tools:

Query Tools: help users to find data using certain inputs.  Also, they allow users to zoom in on specific data points or zoom out to see overall trends.

Analytics Tools: use statistical methods to predict future trends and uncover hidden trends. Ideally, most advanced tools leverage artificial intelligence and machine learning algorithms to provide more accurate analytics.

Dashboards and Data Visualization Tools: allow users to present data visually. 

Data Mining Tools: assist users to dve deep into the data to extract valuable insights.

Understanding these components and their roles helps you appreciate the complexity of a data warehouse and the value it provides in turning raw data into actionable insights.

Different Types of Data Warehouse Architecture

The warehouse architecture defines the arrangement of data in different databases. The structure of the data warehouse identifies the most efficient technique for organizing data. There are three main approaches to constructing a data warehouse: 

Single-Tier Architecture

Also called standalone architecture is a type of data architecture set on a single server or system. Has all the necessary components, i.e ETL tools and database management system, and reporting tools in the server. Basically, ideal for smaller organizations with insignificant data volume.

Se single tier architecture is relatively straightforward to implement and manage. However, there are limitations in terms of scalability and performance as the data warehouse grows.

Two-Tier Architecture

Divides the warehouse into 2 layers i.e, client side and data side. The server stores and manages the data, while the client layer has all the necessary end user tools for access and analytics. A data mart level is added between the user and data sides.

All in all, it allows better scalability. Well, it distributes the storage and processing across multiple servers. Unlike the single-tier, the two-tier has a staging area that ensures the data you load is well formatted. More common with businesses that use data marts as a server.

Three-Tier Architecture

Also known as Web-Based Architecture, is commonly used in large enterprises. Organizations usually add an online analytical processing (OLAP) cube on top of the data mart. The OLAP cube represents data in such a way you compile it from multiple dimensions.

This architecture further separates the data warehouse into 3 layers: bottom, middle, and top tiers. The bottom layer includes databases, external systems, and flat files. Middle tier, data is gets transformed and aggregated, alongside other operations. The top tier layer consists of web browsers or desktop applications through which end users access and analyse the data.

Well, this data tier offers more scalability, especially if you have large datasets. Essentially, each layer is independently scaled and maintained, and this offers higher levels of flexibility. Thanks to web based access, it’s also more accessible to a wide range of users.

Key Concepts in Data Warehouse Architecture

When working with a data warehouse, you most likely come across lots of concepts that you need to understand. Here are some of them explained:


OLTP and OLAP are data processing systems to manage large amounts of data. The main difference is that one is purely operational while the other provides valuable insights.

OLTP (Online Transaction Processing): designed for real time transactional processing. Characterized by a large number of short online transactions (INSERT, UPDATE, DELETE). Ideally, the main focus of OLTP systems is maintaining current data to manage the daily operations of an organization. Basically, OLTP systems are ideal for order processing, customer service, and inventory management.

OLAP (Online Analytical Processing): In contrast, OLAP systems analyse and optimize for retrieving and analysing historical data. They have relatively low volume of transactions but complex queries involving aggregations over a wide range of user oriented data. These systems help with tasks such as predictive analytics, data mining, and business intelligence.

Data Schema

Schema is a common data warehouse concept suitable with SQL databases. Basically, it is a logical description of how data is stored in the warehouse. It defines the relationship between tables and columns, as well as the structure of the warehouse. This makes it easy to query and analyze data. 

There are 3 types of a data warehouse schema:

Star Schema: Simplest style of data mart schema. In a star schema, there is one fact table referencing any number of dimension tables. It’s called a “star schema” because the entity relationship diagram of this schema resembles a star, with points coming from a central table.

Snowflake Schema: More complex database schema. It’s a variation of the star schema where the dimension tables are normalized to eliminate redundancy. This normalization splits up the data into additional tables, which is why it’s called a “snowflake” schema, as the diagram resembles a snowflake.

Galaxy Schema: Variation of the snowflake schema. It consists of multiple fact tables sharing dimension tables, viewed as a collection of stars. The dimension tables are further divided into much smaller tables to reduce redundancy and improve performance.

The type of data warehouse schema largely depends on the size of data and complexity of the warehouse.

Data Cubes and Multidimensional Modeling

A data cube is a multidimensional construct used for data storage in a data warehouse. This structure arranges data for faster querying and analysis. Commonly employed in online analytical processing (OLAP), a form of business intelligence, data cubes empower users to swiftly and effortlessly analyze vast volumes of data.

Hence, data cubes and multidimensional modelling enhances business performance. They also help in accelerating sales, trimming costs, and augmenting customer service.

Data Mining vs Predictive Analytics

Both data mining and predictive analytics are techniques used to extract knowledge from data.

Data Mining: discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Essential process where intelligent methods are applied to extract data patterns. Finally, the discovered patterns are used to make predictions about future events.

Predictive Analytics: encompasses a variety of statistical techniques, from predictive modelling and machine learning. All these techniques analyse current and historical facts to predict future or unknown events. Predictive analytics make predictions about future events such as sales, business risk, or customer behavior.

Data Marts vs Data Lakes

Data Mart: a structure/access pattern specific to data warehouse environments that retrieve client-facing data. Commonly, a data warehouse subset and is usually oriented to a specific business line or team.

Data Lake: a large storage repository that holds a vast amount of raw data in its native format. While a hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data. One of the main benefits of a data lake is the ability to store all types of data (structured, semi-structured, and unstructured) and the flexibility to perform all types of processing – from batch to real time.

Thank you for reading
Data Warehouse Architecture: Types, Components, & Concepts. We shall conclude this article now.

Data Warehouse Architecture: Types, Components & Concepts Conclusion

Finally, data warehouses provide the foundation for BI activities, including predictive analytics, trend analysis, and data comparison over time. They enforce data consistency by integrating data from multiple sources, thereby enhancing the quality of business insights derived. 

With data warehouses, businesses access historical data, analyse trends, and make forecasts, leading to improved strategic, tactical, and operational decisions. Lastly, Data warehouses are optimized for read access, ensuring swift data retrieval, which is vital in today’s fast paced business environment.

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Dennis Muvaa

Dennis is an expert content writer and SEO strategist in cloud technologies such as AWS, Azure, and GCP. He's also experienced in cybersecurity, big data, and AI.

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