Generalities
A Data Store (also called Operational data Store or ODS) is an intermediate solution between source systems and a data solution (data warehouse, data lake...).
It is a staging area for integrating and consolidating data from multiple operational sources before loading it into the data warehouse.
Unlike a standalone database, a Data Store is designed to work in conjunction with other data architecture components.
The primary function of a Data Store is to provide a near real-time, integrated view of operational data from various sources.
It typically contains current or very recent data, often with some historical context, but not the extensive history in a data warehouse.
Data in a Data Store is usually cleansed, transformed, and standardized to ensure consistency across different source systems.
Benefits
Benefits of a Data Store:
- Data Integration and Standardization: A Data Store consolidates data from multiple sources, applying consistent business rules and quality standards. This ensures that data entering the data warehouse is clean, consistent, and properly integrated.
- Real-Time Operational Reporting: While primarily serving as a staging area, a Data Store can support some operational reporting needs, providing a current view of business operations without impacting the performance of source systems or the data warehouse.
- Reduced Load on Source Systems: By acting as an intermediary, a Data Store can offload some query processing from operational systems, helping to maintain their performance for critical business processes.
- Simplified ETL Processes: A Data Store can simplify the Extract, Transform, and Load (ETL) processes for the data warehouse by pre-integrating and standardizing data, reducing the complexity of warehouse loading operations.
Drawbacks
Drawbacks of Data Stores in the context of a broader data architecture:
- Additional Complexity: Implementing a Data Store adds another layer to the data architecture, which can increase overall system complexity and maintenance requirements.
- Potential for Data Latency: While a Data Store provides more current data than a data warehouse, there can still be some latency between real-time operational data and what's available in the Data Store, depending on update frequencies.
- Limited Historical Analysis: As a Data Store focuses on current operational data, it may not support extensive historical analysis, which remains the data warehouse domain.
- Resource Intensive: Maintaining a Data Store requires additional hardware, software, and human resources, which can increase the overall cost of the data management infrastructure.
Conclusion
A Data Store is a crucial intermediary between source systems and a data warehouse, facilitating data integration, standardization, and operational reporting capabilities.
While it adds value by providing a near real-time, integrated view of operational data and simplifying warehouse loading processes, it also introduces additional complexity and resource requirements to the overall data architecture.
Organizations should consider their data integration needs, reporting requirements, and available resources when implementing a Data Store as part of their broader data management strategy.
To review all the modeling approaches, please look at Model a BI solution.
Available Generators
To use Data Store modeling, please use one of the following Generators: