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In the context of Interfaces, they function as an internal extract, transform, load (ETL) mechanism. For example, this is a metadata crosswalk from MARC standards to Dublin Core : MARC field
Another way that companies use ETL is to move information to another application permanently. For instance, the new application might use another database vendor and most likely a very different database schema. ETL can be used to transform the data into a format suitable for the new application to use.
Staging area architectures range in complexity from a set of simple relational tables in a target database to self-contained database instances or file systems. [4] Though the source systems and target systems supported by ETL processes are often relational databases, the staging areas that sit between data sources and targets need not also be ...
The design of forms for automated database testing, form front-end and back-end, is helpful to database maintenance workers. Data load testing: For data load testing, knowledge about source database and destination database is required. Workers check the compatibility between source database and destination database using the DTS package.
Update database and/or update model Multi-user collaboration using File, DBMS or Cloud Repository (or transfer via XMI, CVS/TFS or Difference Merge). ER/Studio: Logical, physical, ETL IDEF1X, IE (Crow’s feet) Yes Yes Update database and/or update model
An operational data store (ODS) is used for operational reporting and as a source of data for the enterprise data warehouse (EDW). It is a complementary element to an EDW in a decision support environment, and is used for operational reporting, controls, and decision making, as opposed to the EDW, which is used for tactical and strategic decision support.
Extract, load, transform (ELT) is an alternative to extract, transform, load (ETL) used with data lake implementations. In contrast to ETL, in ELT models the data is not transformed on entry to the data lake, but stored in its original raw format.
Data virtualization is an approach to data management that allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted at source, or where it is physically located, [1] and can provide a single customer view (or single view of any other entity) of the overall data.