An operational data store (or “ODS”) is a database designed to integrate data from multiple sources for additional operations on the data. The data is then passed back to operational systems for further operations and to the data warehouse for reporting. Because the data originates from multiple sources, the integration often involves cleaning, resolving redundancy and checking against business rules for integrity. An ODS is usually designed to contain low-level or atomic (indivisible) data (such as transactions and prices) with limited history that is captured “real time” or “near real time” as opposed to the much greater volumes of data stored in the Data warehouse generally on a less-frequent basis.
The general purpose of an “ODS” is to integrate data from disparate source systems in a single structure, using data integration technologies like data virtualization or data federation. This will allow operational access to the data for operational reporting, master data or reference data management. An “ODS” is not a replacement or substitute for an enterprise data warehouse but in turn could become a source. (Source: Wikipedia)
An ODS involves:
- Extracting data from operational systems;
- Moving it into ODS structures; and
- Reorganizing and structuring the data for analysis purposes
How is an ODS different from a data warehouse?
A data warehouse is intended to support strategic planning and business intelligence decision support. It should contain:
- Integrated subject oriented data, e.g sales data;
- Static data, e.g. data that is moved into data warehouses should not change after it is stored in the data warehouse environment;
- Historical data, e.g. data warehouses will usually contain several years worth of historical data; and
- Aggregated, or summarized data e.g. as data becomes “older”, it is summarized to reduce data storage requirements and to improve analysis performance.
An operational data store is intended to support operational management and monitoring and should contain:
- Integrated subject oriented data (similar to data warehouses) e.g sales data;
- Volatile data, e.g. data that is moved into an ODS will probably change frequently;
- Current data, e.g. an ODS will usually contain several weeks or even months worth of data instead of large volumes of historical data; and
- Detailed data e.g. as data becomes “older”, it is summarized to reduce data storage requirements and to improve analysis performance. (Source: Information Management Architect)
Joshua is working to become a Data Scientist with focus on Analytics, Big Data, Machine Learning, and Statistics. His passion for Data and Information are second to none. He is a certified IBM Cognos Expert with more than 10 years experience in Business Intelligence & Data Warehousing, Analtyics, IT Management, Software Engineering and Supply Chain Performance Management with Fortune 500 companies. He has specializations in Analytics, Mobile Reporting, Performance Management, and Business Analysis.
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