Operational data store versus a data warehouse

Data Warehouse is a repository of integrated information, available for queries and analysis. Data and information are extracted from heterogeneous sources as they are generated....This makes it much easier and more efficient to run queries over data that originally came from different sources.
Typical relational databases are designed for on-line transactional processing (OLTP) and do not meet the requirements for effective on-line analytical processing (OLAP). As a result, data warehouses are designed differently than traditional relational databases.


Operational Data Store is a collection of operation or bases data that is extracted from operation databases and standardized, cleansed, consolidated, transformed, and loaded into an enterprise data architecture. An ODS is used to support data mining of operational data, or as the store for base data that is summarized for a data warehouse. The ODS may also be used to audit the data warehouse to assure summarized and derived data is calculated properly. The ODS may further become the enterprise shared operational database, allowing operational systems that are being reengineered to use the ODS as there operation databases.

You will find many conflicting opinions on this. The following is the opinion shared by me and many of my consulting colleagues within The Data Warehouse Institute, of what the difference SHOULD be. Enjoy.

The operational data store lives in the operational support system environment. It typically serves the purpose of providing "near" real-time integration and reporting of data across disparate operational systems. It is designed for update. It is fed by operational support sytems, AND it will feed those systems. It is NON-historic. Many times operational applications get built upon the ODS structures. That ends the significant differences from a data warehouse. The following charactersitics are shared between an ODS and a DW. It is subject oriented, it is highly normalized. The data integration is enables using the same suite of ETL tools and EAI tools that enable the data warehousing environments.

The data warehousing environment lives seperate from the operational support systems environment. It serves the purpose of decision support, historical data mining, trendings, etc. It can be updated near real-time, but usually is updated on a premeditated scheduled frequency. It has architectural layers designed in 2 OR 3 tiers to support 3 roles: intake, distribution and access. It is designed for read only. It contains history. It is subject-oriented. The intake layer is normalized, the distribution layer introduces dimensionality and denormalization. The access layer consists of a suite of data marts designed for specific purposes (for trending analysis, etc), some relfecting star schemas others reflecting normalized schemas (for list management and reporting). It is loaded via ETL tools and EAI tools. It is typically accessed using BI tools.