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Delivering data — where and when you need it
SR·Transport DeployedSample Scenarios OverviewRepurposing data for effective use is critical to the data's overall value. The ability to extract data from disparate sources, transform data to be relevant to solving a wide range of problems and load data into new data stores presents challenges addressed by SRTransport.This paper briefly describes scenarios in which extraction, transformation and loading of data by SRTransport plays a critical role.
Scenario 1: Receiving and deploying data
Receiving and deploying data XML is fast becoming the data transmission protocol of choice. That means that as data is received for deposit into local data stores, the data must be extracted from the XML document, transformed into a format that the data store recognizes and loaded. The target may be multiple stores, for example, large scale repositories used for data warehousing and archiving and smaller scale repositories used for data mart preaggregation for reporting.
Scenario 2: Restructuring and relocating data
Restructuring and relocating data Corporate data is often in large corporate stores in formats that don't support reporting and analysis. SRTransport can be used to extract data from a central store, transform it for use by different types of repositories that may be used for summary reporting and in-depth analysis.
Scenario 3: Remote extraction and transmission of data to a reconciliation engine
Remote extraction and transmission of data to a reconciliation engine Businesses working together as part of a supply chain have their own ERP systems carrying their data about transactions. Depending on the terms of the partnership, there may be value in reconciling transactions across ERP systems. SRTransport can be used to play a fundamental part in this process by handling the extraction, transform and load data problems. SRTransport can be deployed as an agent, extracting data from ERP systems and converting it into XML documents representing transactions or other operations. The XML is then transmitted to the reconciliation engine, where SRTransport can be used to transform the XML into structures used by the reconciliation engine. SRTransport then loads data into the engine, which does it's processing. Then SRTransport is used to extract data from the repository associated with the reconciliation engine, transforming and loading data into a DataWarehouse for long term archiving and transforming and loading data into a DataMart for short term reporting on transaction reconciliation status. Although this example is centered around ERP reconciliation, it demonstrates using SRTransport in a cluster of highly dynamic, multiple source and destination large scale data processing problems.
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