What is an operational data store (ODS)?

Introduction

An operational data store (ODS) is a database that integrates data from multiple sources and combines them into a central representation, presenting a current snapshot of the business's data.

ODS is used for lightweight data processing activities, such as operational reporting and real-time analysis. It also supports business intelligence (BI) tools that help with strategic decision-making.

Characteristics

Multiple data sources are integrated to create an ODS. Each data source must have the following properties:

• Subject-oriented: ODS should be built on the functional requirements of an organization. It revolves around the organization's significant information subject—for instance, students, lecturers, and courses will be the subjects in a university.
• Integrated: Data from various sources are consolidated in an ODS after they undergo the ETLExtract, transform, and load process, where data is extracted from the sources, transformed, and loaded to the destination.
• Up-to-date: ODS contains a snapshot of the organization's current data.
• Volatile: Frequently replacing old data with new data makes ODS super volatile.
• Detailed: The data in ODS is detailed enough to serve the organization's operational needs.

Working

ODS works in a similar way to the ETL process. A staging database is used in the transformation layer to hold raw data from production databases, as shown below:

In the case of an ODS, data is extracted from the production databases but not transformed. It is presented to BI applications for analysis, reporting, and decision-making.

Note: The data in the ODS can be replicated and transported to a data warehouse after going through the ETL process to reduce visits to the production databases.

Uses

ODS can be used for multiple tasks such as:

• Current data snapshot allows real-time analysis and decision-making.
• Useful for BI tasks—monitoring customers, tracking orders, and so on.
• By comparing the ODS data version to the stored data version on other systems, organizations can identify integration issues.
• ODS can synchronize data across multiple systems using triggers.
• Critical, time-sensitive business rules can be integrated into an ODS to perform automatic processes, such as notifying the sales department when a customer places an order.

Difference between an ODS and a data warehouse

ODS and data warehouses import and consolidate data from multiple sources. However, both differ from one another in numerous aspects.

Schema

A data warehouse has schema-on-write, meaning incoming data goes through the ETL process, which cleanses, harmonizes, and organizes data according to the data warehouse schema. In an ODS, only structured data is stored, therefore, it remains in its existing schema.

Decision-making

A data warehouse has access to all the historical data. Hence, it helps in long-term decision-making. On the other hand, ODS stores limited, current data that allows real-time and strategic decision-making.

Size and data scope

A data warehouse has a larger size and grows exponentially because it stores all the historical as well as the incoming data. On the other hand, an ODS has a comparatively smaller size and grows linearly because it overwrites existing data when new data comes in.

Volatility

A data warehouse's contents change less often than an ODS's, which changes frequently.

Query complexity

A data warehouse can execute complex queries on a vast amount of data. An ODS can execute simple queries on a limited amount of data.

Note: ODS sits between the data sources and a data warehouse.

Conclusion

An ODS is a database that stores the organization's current data snapshot to help in operational and real-time decision-making.