What is Data Mapping?
Data mapping facilitates the integration of data between systems with different formats, structures, or standards. It provides a mechanism to translate or transform data from the source system into a format that the target system can understand. This compatibility ensures that information can flow smoothly across systems, regardless of differences in data representations.
One of the key tasks of any integration is to define how data is transferred, or mapped, between the two applications.
Mappers are typically used to create mappings between source and destination applications. Mapper enables to mapping of element nodes between applications by dragging source element nodes to target element nodes. On the left, it represents the data structure of the source element node and on the right, it represents the data structure of the target element node.
Fig.1: Description of Mapping
Mapping Components and Their Roles:
A mapping window will appear in Mapper integration as shown (Fig 2).
- Designer: It returns to the mapping canvas from the Code, Test, or Recommends page.
Fig.2: Child Element shown with Name
- Code: Displays the XSLT code generated during the mapping design.
Fig.3: Mapper Code
- Test: Once the mapping design is completed, testing can be conducted by inputting a sample payload to process the mapping.
Fig.4: Mapper test
- Recommend: Enable the Recommendations Engine, which accepts the engine’s target element recommendations when creating the mapping. This removes the requirement to manually figure out and match each source with its corresponding destination.
Fig.5: Mapper recommend
- Developer: Chooses to show or hide the view that displays technical schema names.
Fig.6: Without Enable Developer
Fig.7: With Enable Developer
- XSLT: Allow to see XSLT parts (figure – 8)
Fig.8: XSLT mapper
- View: It contains two options –
- Select to show the name-space prefixes on the source and target element nodes.
- Select to show the types (prefixes and data types) on source and target element nodes.
Fig.9: View Mapper
- Filter: Filter the display of element nodes such as error messages and warnings in the source or target element node.
Fig.10: Filter Options
How To Perform Data Mapping Between Applications?
A mapper is like a translator that helps match how information comes in (source) to how it goes out (target) in different applications. It’s like making sure the right puzzle pieces fit together correctly.
Fig.11: Before Mapping Elements
Follow the below steps to perform mapping:
- Map fields from the source element and drag them to the corresponding target element node.
Fig.12: XPath Expression
- A blue line connects the two nodes. An expression builder below the mapper is displayed to show the XPath expression.
- XPath Expression: In the Expression build section, you can move the source element and use functions or XSLT statements to modify the XPath expression if needed.
- Use the Toggle Functions option to incorporate functions, operators, or XSLT statements into the mapping.
Fig.13: Toggle Function
- Validate the mapping by clicking the Validate button.
- Click on the Close and then Save button.
Benefits Of Data Mapping:
Certainly, here are the points rewritten for clarity and conciseness:
- Data Consistency: Data mapping ensures that information is consistently represented and accurately transferred between different systems. It aligns data formats, structures, and meanings, reducing the chances of errors or inconsistencies during integration.
- Efficiency: Data mapping streamlines integration development, allowing organizations to effortlessly merge data from diverse sources, thereby decreasing the need for manual data entry.
- Enhanced Insights: Accurate and consistent data mapping empowers organizations to create comprehensive reports and conduct in-depth analysis across integrated systems.
- Scalability: Data mapping enables businesses to expand their operations and accommodate future growth. When new systems or applications are introduced into the technology landscape, data mapping provides a framework for seamless integration with existing systems.
- Mapper Code Access: The Mapper Code option grants access to the underlying code of your data mapping, offering more control and customization.
Limitation Of Mapping:
- Mapping takes a long time when handling big sets of data.
The Bottom Line:
Data mapping is a cornerstone of effective integration in the Oracle Integration Cloud. By aligning data across systems and facilitating seamless data transfer, it enhances operational efficiency and data accuracy. While it has its limitations, particularly in dealing with large datasets, the benefits of data mapping are substantial, making it an indispensable tool for businesses seeking efficient and accurate data integration.
Author: Abhishek Narayan Pandey, OIC Consultant