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What is a Data Warehouse Architecture?
Imagine you are the IT director of a mid-sized company. Your responsibility is to ensure that all departments—from finance to marketing—have access to the data they need to make informed decisions. But suddenly, complaints start piling up: reports take too long to generate, data is inconsistent, and the IT department is overwhelmed with requests. Sound familiar?
This is where data warehouse architecture comes into play. It serves as the backbone of any data-driven organisation, determining whether data becomes a burden or a valuable asset. In this blog post, you will learn what data warehouse architecture is, why it is so important, and how modern solutions can help overcome today’s challenges.
A data warehouse architecture is the structural framework that defines how data is collected, stored, processed, and used within an organisation. It consists of multiple layers and components working together to integrate, cleanse, and prepare data from various sources for analysis.
The architecture can vary depending on the organisation’s needs—from simple single-layer designs to complex, distributed systems. However, the ultimate goal remains the same: transforming data into actionable insights.
Data Warehouse Architectures can be divided into two main categories:
1. Structure-Oriented Architectures – These differ based on the number of layers used:
- Single-Layer Architecture
- Two-Layer Architecture
- Three-Layer Architecture
2. Function-Oriented Architectures – These differ based on the type of main components:
- Independent Data Marts
- Bus Architecture
- Hub-and-Spoke Architecture
- Centralised Architecture
- Distributed Architecture
Below, we introduce the different architectures and explain their characteristics, as well as their advantages and disadvantages.
Structure-Oriented Architectures
Single-Layer Architecture
This architecture is rarely found in practice. Its primary goal is to avoid redundancies by minimising data storage. However, a major weakness of this structure is the lack of separation between analytical and transactional data processing. As a result, it is not suitable for complex analyses or large volumes of data.
Two-Layer Architecture
This architecture consists of a source system layer and a data warehouse layer, separated by a staging layer for all data sources. This ensures that all data is properly cleansed and formatted before being loaded into the data warehouse. However, this architecture is less scalable and is better suited for small to mid-sized companies.
Three-Layer Architecture
This architecture divides the structure into three physical layers:
Source System Layer – Capturing raw data from various systems.
Transformation Layer – Cleansing, aggregating, and converting data into operational information.
Data Warehouse Layer – Storing and providing data for analytical purposes.
This form is the most widely used and is particularly suitable for large companies that require high data quality and integration.
Function-Oriented Architectures
Independent Data Marts
This architecture consists of several independent data marts that have been developed separately. Since there is no integration between them, they often have inconsistent data structures, making enterprise-wide analysis difficult. This architecture is usually replaced by a better-integrated solution.
Bus Architecture
The Bus Architecture, recommended by Ralph Kimball, is similar to the architecture of independent data marts, but with a significant improvement: the data marts are logically integrated, allowing for an enterprise-wide view of the data.
Hub-and-Spoke Architecture
This architecture consists of:
- A central Enterprise Data Warehouse (Hub) that stores all raw data.
- Multiple Data Marts (Spokes) that are supplied with cleansed, aggregated data from the central warehouse.
- A Transformation Layer that serves as an intermediate storage for normalised data.
This architecture is particularly scalable and is suitable for companies dealing with large volumes of data.
Centralised Architecture
The centralised architecture, recommended by Bill Inmon, is a specific form of the Hub-and-Spoke Architecture. The key difference is that there are no dependent data marts. Instead, a central data warehouse contains all the data and makes it available for analysis. This architecture offers high data quality and integration but can incur high implementation costs.
Distributed architecture
The distributed architecture is used when multiple existing data warehouses or data marts need to be integrated. This is achieved through:
- Joint Keys
- Global Metadata Management
- Distributed Queries
This architecture is particularly suitable for companies with distributed locations or dynamic requirements.
The choice of the right data warehouse architecture depends on various factors, including scalability, integration requirements, and existing IT infrastructures.
While centralised architectures offer high data quality, distributed architectures provide greater flexibility. Companies should carefully evaluate their individual requirements before deciding on an architecture.
Why is Data Warehouse Architecture important?
In today’s data-driven business world, data warehouse architecture forms the backbone for informed business decisions. Companies face the challenge of integrating a variety of data from different sources and making it available in a consistent format.
A well-designed architecture ensures that this data is of high quality, up-to-date, and traceable, providing the foundation for accurate analyses and strategic decisions.
The central role of a data warehouse lies in enabling decision-makers not only to access historical and current data quickly but also to lay the groundwork for the use of modern Business Intelligence (BI) tools. By consistently structuring and preparing the data, relationships that would otherwise be lost in data chaos become visible. Ultimately, this results in optimised decision-making and increased competitiveness, as data-driven strategies can be developed more quickly and efficiently.
How is a Data Warehouse structured?
The construction of a data warehouse architecture takes place in several clearly defined steps to ensure that data is efficiently captured, processed, and made available for analysis.
First, raw data is collected from various source systems, including internal databases, external applications, and other digital data sources. This data forms the basis for all analyses.
Next, data integration is carried out through the ETL process (Extract, Transform, Load). In this step, the data is extracted, cleaned, and converted into a uniform format. A staging area serves as an intermediate storage to check the data quality before it is loaded into the central data warehouse.
The core of the architecture is the database, which is specially optimised for analytical queries. The data is structured into fact tables and dimension tables to enable fast and accurate analysis. Additionally, data marts are used – subject-specific data areas that specifically support individual departments and provide detailed insights.
The final step is the BI layer, which serves as the interface between the prepared data and the users. Modern BI tools enable the creation of interactive dashboards, reports, and visualisations that support data-driven decisions.
With this structured architecture, companies can not only process and store large amounts of data efficiently but also continuously optimise and scale their analysis processes.
7 Success Factors for Building a Modern Data Warehouse Architecture
Scalability and Performance
A modern data warehouse architecture must be able to grow with the needs of the business. On-premise solutions like SAP BW/4HANA provide sufficient performance for near-real-time processing of all data, thanks to their in-memory technology. Cloud-based solutions, such as SAP Datasphere, offer significant advantages in terms of flexibility, as they can scale without additional hardware.
Scalability applies not only to the volume of data but also to the number of users and the complexity of queries. A well-scalable architecture allows for both vertical growth (by adding additional resources to an existing system) and horizontal growth (by adding additional servers).
Seamless Data Integration
Data comes from various sources and needs to be harmonized. Solutions like SAP Datasphere enable seamless integration of data from different systems, allowing businesses to gain a unified view of their data. Integration involves not only the technical merging of data but also ensuring that the data is consistent and of high quality. This often requires the use of ETL (Extract, Transform, Load) tools that extract data from various sources, transform it, and load it into the data warehouse.
Real-Time Analytics
Real-time analytics is a must-have today. Modern data warehouses provide the ability to process and analyze data in real time, giving businesses a competitive edge. Real-time analytics allows companies to quickly respond to changes in the market or customer behavior. This requires an architecture that can handle large data volumes in real time without compromising performance.
Cost Efficiency
By leveraging cloud solutions, companies can significantly reduce the costs of data processing. Pay-as-you-go models and the ability to dynamically allocate resources make the cloud a cost-effective option. Additionally, businesses can achieve further cost savings by utilizing open-source tools and automating processes.
Data Protection and Compliance
With the increasing importance of data protection regulations such as the General Data Protection Regulation (GDPR), it is crucial that the data warehouse architecture meets security and compliance requirements. Modern solutions offer integrated security features and help ensure legal compliance. This includes data encryption, access controls, and regular data security audits.
Cloud and Hybrid Solutions
The cloud offers flexibility and scalability, but not every company can or wants to fully transition to the cloud. Hybrid solutions that combine on-premise and cloud systems offer the best of both worlds. These architectures allow companies to leverage the benefits of the cloud while keeping sensitive data stored locally.
Self-Service BI and Usability
Self-service BI tools enable end users to independently generate reports and perform analyses without relying on the IT department. This increases usability and relieves the IT team. Tools like Power BI and especially SAP Analytics Cloud provide intuitive user interfaces that allow even technically less-experienced users to conduct complex data analyses.
Modern data warehouse solutions from SAP
SAP BW/4HANA
SAP BW/4HANA is based on the powerful in-memory technology of SAP HANA and follows a multi-layered architecture that is specifically optimized for analytical applications. The core of the architecture is divided into the following layers:
- Data Acquisition and Extraction
Data is extracted from various source systems, such as ERP systems like SAP S/4HANA or other relational databases. SAP-specific or generic extractors are used to ensure consistent and efficient data transfer.
- Staging and Transformation
After extraction, the data is temporarily stored in a staging area. Here, the ETL process (Extract, Transform, Load) takes place, where data is cleaned, transformed, and converted into a uniform format. This step ensures data quality and prepares it for further modeling.
- Data Modeling
The transformed data is primarily organized into aDSOs (advanced DataStore Objects) as InfoProviders. These models enable the representation of business processes in terms of key figures, characteristics, and hierarchies, tailored specifically to analytical questions.
- Reporting and Analysis
At the top level, the modeled data is visualized and analyzed through reporting tools and business intelligence solutions, such as the SAP Analytics Cloud. This layer provides end users with intuitive options for creating reports and dashboards.
As a guideline for building a BW/4HANA Data Warehouse, the reference architecture LSA++ (Layered Scalable Architecture) is available. It optimizes the classic LSA architecture by reducing redundant data persistence and placing a stronger emphasis on virtual data models. The layers of the architecture—from data acquisition and transformation to analysis—become leaner and more efficient. By using Advanced DataStore Objects (aDSOs) and Composite Providers, LSA++ enables powerful, flexible, and maintainable data processing, specifically tailored to the in-memory technology of SAP HANA.
SAP Datasphere
As a central component of the SAP Business Data Cloud, SAP Datasphere represents a modern, cloud-native approach to data warehousing. Its architecture is designed to unify heterogeneous data sources—both SAP and non-SAP systems—into a single, flexible data model. The key architectural components are:
- Data Integration and Virtualisation:
Instead of fully replicating data, SAP Datasphere enables a federated data architecture. Using data virtualization and modern connectors, data can be integrated in real-time or at regular intervals. This reduces administrative effort and ensures data is always up to date.
- Semantic Modeling and Data Catalog:
A central feature is the Business Data Fabric approach, in which data is harmonized through a unified, semantic layer. With the help of an integrated data catalog, data sources can be classified, linked, and provided with business context, making it easier for end users to intuitively access information.
- Spaces and Self-Service:
SAP Datasphere uses the concept of “Spaces,” where specific data models and use cases are represented. These environments allow both IT and business departments to independently model and analyze data. This fosters agile self-service analytics, relieving traditional IT processes.
- Seamless Integration with BI Tools:
A key advantage of SAP Datasphere is the concept of Seamless Planning, which ensures an uninterrupted connection between data analysis and planning. Through tight integration with the SAP Analytics Cloud, companies can not only analyze historical and current data but also create and adjust planning scenarios directly within the same system. This seamless connection of data integration, modeling, and planning ensures agile and collaborative decision-making without the need for manual data movement or duplication.
With this modern architectural approach, SAP Datasphere offers businesses the flexibility to respond to dynamic market demands while maintaining a central, consistent data foundation—a critical advantage in today’s data-driven economy.
Both architectures address different needs: While SAP BW/4HANA focuses on proven, structured data processing in existing SAP environments, SAP Datasphere, as a cloud-based solution, opens up new opportunities for data integration, flexibility, and self-service analytics.
SAP Business Data Cloud
The way companies store and analyze data is rapidly evolving. While traditional data warehouse architectures are still important, new technologies offer more flexibility and efficiency. Particularly exciting is the shift towards the Lakehouse model, which combines the benefits of data warehouses and data lakes.
What is a Lakehouse?
Traditional data warehouses are powerful when it comes to processing structured data, meaning data that exists in clearly defined tables and columns. They guarantee high data quality but are often costly and less flexible when dealing with large volumes of data or unstructured data such as images, videos, or log files.
A data lake, on the other hand, can store massive amounts of data—whether structured, semi-structured, or unstructured. The downside is that without additional technology, there is no clear data structure, which makes analysis difficult.
The Lakehouse combines the best of both worlds:
- Structured and unstructured data can be managed together.
- Data quality and consistency are ensured through modern technologies like Delta Lake.
- Real-time analytics and artificial intelligence (AI) can be directly applied to the stored data.
SAP Business Data Cloud and the future of Data Analytics
SAP takes it a step further with the SAP Business Data Cloud, making the Lakehouse model accessible to SAP customers. A key component in this is SAP Datasphere.
A major advantage is the direct integration with Databricks Delta Lake and Apache Spark:
- Delta Lake ensures that data remains structured, traceable, and consistent even in an open data lake.
- Apache Spark enables the rapid analysis of vast amounts of data—both in real-time and for complex machine learning models.
- Seamless Integration: SAP customers can link their existing systems to modern data platforms without having to overhaul their entire architecture.
Through this innovative integration, SAP offers a future-proof solution for businesses that want to leverage the best of traditional data warehouses and modern data lakes within a flexible and scalable Lakehouse model.
Conclusion
The data warehouse architecture remains the central foundation for data-driven companies—but it is evolving. While traditional architectures still provide stable, structured analyses, modern solutions like lakehouse models offer the flexibility needed for growing data volumes and new technologies like real-time analytics, artificial intelligence (AI), and machine learning.
With the SAP Business Data Cloud and SAP Datasphere, SAP provides a future-proof platform that integrates both classic data warehouse structures and data lakes, along with AI-driven analytics. Companies benefit from an open, scalable architecture that can be flexibly adapted to new requirements without having to completely replace existing systems.
The future belongs to hybrid and connected architectures—companies that embrace modern data warehouse concepts today are laying the groundwork for faster, smarter, and more competitive decision-making.
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