Over the past few years, enterprise data architectures have evolved significantly to accommodate the changing data requirements of modern businesses. The emergence of advanced data storage technologies, such as cloud computing, data hubs, and data lakes, makes us question the role of traditional data warehouses in modern data architecture. 

Data warehouses were first introduced in the 1990s as relational data storage spaces for decisional support systems; however, they have undergone considerable change over the years to support business intelligence (BI) and data-driven decision-making. 

Are they still relevant for modern enterprises today? We must go back to the basics to find an answer to this question.

The Purpose of Data Warehouses

A data warehouse (DWH) is a centralized repository that consolidates and stores historical data from disparate sources to provide a holistic view of organizational data. It contains structured and standardized data to meet the business reporting needs efficiently.

A DWH contains data from internal sources, such as CRMs, ERPs, financial systems, and external systems, like partner programs. Technically, the data is then structured and processed per the DWH’s configuration before being stored in a consolidated form and made available for BI reporting. 

Benefits of Using a Data Warehouse

The primary purpose of the data warehouse is to provide organizations with a centralized repository for otherwise heterogeneous sources and make data accessible for fast and accurate BI reporting. 

How does it do this?

• Serves as a single source of truth: As mentioned above, a data warehouse acts as a central platform for all your data, which is otherwise scattered around in the form of distributed excel sheets and disparate transactional systems. A functional data warehouse relies on data modeling and ETL/ELT processes to integrate heterogeneous data into a single place. And through this integration, companies can view data from different functional and transactional areas, e.g., operations, sales, finance, and marketing, in unison to gain a 360-degree view of their organization.
• Eliminates data silos: Data exists in silos, trapped within different databases, making it more susceptible to discrepancies and ambiguities. Without a data warehouse, it becomes challenging for business analysts and decision-makers to manage relevant data from different sources, either manually or through queries. A single source of truth – in the form of a data warehouse – eliminates data silos and improves the overall quality of your BI decision-making.
• Provides a pre-defined structure to match reporting needs: A data warehouse stores data in a structured format. It uses data modeling, such as dimension modeling, to define relationships between data points and entities based on the specific reporting use case. The data warehouse schema is optimized for faster data retrieval and querying. 

Furthermore, the data is cleansed, profiled, and transformed before its loaded into a data warehouse. This structured data is analysis-ready and can seamlessly be consumed by BI software, such as PowerBI, to enable business intelligence.

Having structured data and schema in a data warehouse, as opposed to data lakes, eventually makes it easier to build reporting queries, especially in complex relationships and transformations. 

Are Data Warehouses Still Relevant? 

Modern data architecture technologies have transformed how we interact with data within an organization. The advent of big data, data lakes, data fabrics, artificial intelligence, and machine learning has allowed us more opportunities to utilize data for analytics and decision-making effectively. 

Does this affect the relevance of a data warehouse in modern architecture? Absolutely not! In fact, the inclusion of platforms like data lakes in data pipelines facilitates data warehouses and marts in satisfying an organization’s data requirements. 

On top of that, organizations still need a centralized repository for maintaining historical data otherwise existing in heterogeneous systems. Moreover, they still need to access organized, structured data that can be queried easily for timely BI reports. This shows that the data warehouse is still very much relevant. 

It’s no surprise that the data warehousing market is expected to grow to $7.69 billion at a CAGR of 24.5% by 2028. Most data-driven organizations still use a data warehouse as an integral part of their data architectures and will continue to do so in the foreseeable future.