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Saturday, 11 March 2017


Define Operational Data Store (ODS)


By on March 11, 2017

An ODS is an integrated database of operational data. ODS is a subject-oriented, integrated, current-valued and volatile collection of detailed data that provides a true enterprise view of information. ODS is frequently updated (synchronously, hourly, daily or batch processing). The frequency of update and degree of integration depends on the specific business requirements. The detailed current information in the ODS is transactional in nature. An ODS may contain 30 to 60 days of information. ODS can also provide a stepping-stone to feed operational data into the data warehouse. The data warehouse industry has developed ODS, usually presenting it as running on a separate platform, to satisfy tactical decision making requirements.

ODS can deliver operational reports, especially when both the legacy systems and the OLTP systems do not provide adequate operational reports. It relieves production system of reporting and analysis demands and provides access to current data.

ODS can be a third physical system sitting between the operational systems and the data warehouse or a partition of the data warehouse itself. ODS is optional.

ODS can be transient or persistent.

1. Data Presentation

The data presentation area is where the data is stored and optimized for direct querying, reporting, and analysis. It consists of the data warehouse or a series of integrated data marts. For business community, presentation area is the data warehouse. Data in the presentation area strictly is in dimensional form. It must contain detailed, atomic data. It must adhere to the data warehouse bus architecture. While the logical design of presentation area is dimensional model, physical implementation may be in RDBMS or MDDB. If RDBMS is used, data is stored in tables referred to as star schemas. If MDDB is used, then data is stored in cubes.

2. Data Access Tools

Data access tools are used by the business community to query the data warehouse’s presentation area.They may be ad hoc query tools, data mining tools, or application tools. Users may need to perform simple to complex business modeling, complex drill-down, simple queries on prepared summary information, what-if analysis, trend analysis and forecasting and data mining using data spanning several time periods.


3. Metadata

Metadata is information about the actual data itself. Metadata explains what data exists, where it is located and how to access it. It comes in many shapes and forms and it supports the data warehouse’s technical, administrative, and business user groups.

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