An operational data store (ODS) is a central database that provides a snapshot of the most recent data from multiple transaction systems for operational reporting. Operational data stores are designed to integrate data from different sources for lightweight data processing activities such as operational reporting and real-time analysis.
In the ODS, data can be scrubbed, deduplicated, and checked for compliance with the associated business rules. An ODS can be used to integrate data from various sources so that business operations, analysis, and reporting can be performed during business operations.
This is where most of the data used in current operations is housed before being moved to the data warehouse for storage or long-term archiving.
An operational data store typically stores and processes data in real time. An ODS connects multiple data sources and pulls data into a central location. Operational data stores work like the extraction, transformation, and load (ETL) process.
When an ETL-driven data pipeline feeds a data warehouse, there are three stages in the ingestion process: extraction from source systems, data transformation, and loading into the destination. The transformation layer uses a staging database containing raw data from the production databases. This staging database is small and lightweight and only contains the most recently imported data.
ODS systems import raw data from production systems and store it in its original form. Where ETL applies transformation in the second stage, the ODS makes the raw data available to business intelligence (BI) tools for analysis.
Sometimes, the enterprise can replicate data in an ODS for BI purposes and then use an ETL process to move the ODS data to a warehouse. This approach can reduce the load on the production databases that provide the raw data.
When operational data stores ingest data, new incoming data overwrites existing data.
Like data warehouses and data lakes, operational data stores can be used as a repository for importing and consolidating various types of operational data from different locations and systems. However, there are important differences.
An ODS can be used within a data warehousing strategy. It can sit between data sources and enterprise data warehouses, which can be used to prepare data for storage in the data warehouse. The ODS can become a data source for data warehouses.
Below are the key differences between operational data stores and data warehouses.
Perhaps the biggest difference between the two is the volatility of the data. ODS data is extremely volatile, with values that change almost in real time. The content of an ODS can dramatically change from one moment to the next.
A data warehouse is much more stable. Warehouses retain historical values and integrate them with new incoming values. Data warehouse updates usually occur in planned batches, so the content of the warehouse may only change a few times a day.
Data warehouses have a fixed schema and require an ETL process to clean, harmonise, and organise the data according to that schema. Operational data stores store data according to their schema before it is stored. Therefore, operational data stores can only store structured data.
Data warehouses store more historical and cross-functional data that decision-makers can use for strategic analysis. As ODS systems incorporate new data, they overwrite older data, limiting the scope of the data. This makes ODS systems more suitable for data based on the current state rather than long-term strategic planning.
It is important to note that while an ODS is an efficient storage solution for small to medium-sized datasets, it is not ideal for handling large amounts of data, commonly known as big data.
Alternative solutions such as Hadoop or data warehouses are more suitable for big data storage and processing. These solutions are specifically designed to manage large-scale datasets and provide the necessary infrastructure to store and process data effectively.
Data warehouses are designed to handle big data and are optimised for complex queries and analytics. They can integrate data from multiple sources and provide a single, unified view of the data.
With a data warehouse, you can store massive amounts of data, which can be queried quickly and efficiently. Additionally, data warehouses can be scaled up or down depending on your needs, making them a flexible solution.
An ODS is designed for relatively simple queries on small amounts of data, such as finding the status of a customer order. Data warehouses are designed for complex queries on large amounts of data.
An ODS is similar to short-term memory in that it only stores very recent information; in comparison, the data warehouse seems more like long-term memory in that it stores relatively permanent information.
An Operational Data Store (ODS) is a type of database that is designed to help organisations store and manage operational data from multiple sources in a single location. It is used to support real-time operational reporting, business intelligence, and analytics.
An ODS is designed for real-time or near real-time data processing and analysis, whereas a traditional data warehouse is optimised for historical data analysis. An ODS is typically used to support operational reporting and analytics, while a data warehouse is used for strategic decision-making.
Using an ODS can help organisations to improve operational efficiency, enhance decision-making capabilities, and reduce data redundancy. It can also help to provide a single source of truth for operational data, which can improve data quality and consistency.
An ODS should be designed to support real-time or near real-time data processing and analysis. It should be able to integrate data from multiple sources, provide a single source of truth for operational data, and support operational reporting and analytics.
Organisations that have large amounts of operational data from multiple sources can benefit from using an ODS. This includes organisations in industries such as healthcare, finance, retail, and manufacturing.
