Source-to-target mapping in data engineering refers to the process of identifying and defining how data is transferred and transformed from a source system to a target system. It involves mapping the fields in the source system to the fields in the target system and specifying any transformations that need to be applied during the migration process.

For example, let’s consider a scenario where a company wants to migrate its customer data from an old CRM system to a new one. The old CRM system stores customer data in a relational database, whereas the new system uses a NoSQL database.

In this case, the source-to-target mapping would involve identifying the fields in the old CRM system’s database (such as customer name, address, phone number, etc.) and mapping them to the fields in the new NoSQL database (such as customer ID, name, contact information, etc.).

Additionally, transformations might be needed to convert data types or modify data structures during migration. For example, the old CRM system might store dates as strings, whereas the new system requires them to be in a specific date format. The source-to-target mapping would need to specify the transformation to convert the date strings to the required format during the migration.

Source-to-target mapping can help ensure that the data being moved from one system to another is accurate, complete, and consistent. This is important because data quality issues can significantly impact business decisions, timelines, and budgets. When you can be confident that the data arriving at your destination is as expected, you can mitigate the impact of even a large-scale data migration.

The sheer volume and complexity of modern data systems preclude the careful, manual data mapping of the past. It just isn’t reasonable to devote resources towards such a painstaking process, given the unlikelihood that the data you’re documenting will remain unchanged while you observe it. Fast-moving, super large and infinitely complex modern data systems are no longer set up to accommodate manual source-to-target efforts.

Even if the systems in question lent themselves to the task, inadequate documentation can derail a source-to-target effort as quickly as it starts. Very few of us live in a world where our source and target data systems are fully and completely documented. Such faults can lead to gaps in both understanding and functionality. 

Finally, the problem that all manual processes must accept: human error. Source-to-target mapping is primarily a manual process that, by its very nature, is open to typos, incorrect data, and unfounded assumptions. 

Put simply, the modern approach to data mapping is not to do it at all. Modern schema-agnostic tools like StreamSets allow you to send data from an origin to a destination without being explicit about field names, types, or any other feature or particular of source-to-target-mapping. Configure transformations to your data once, then new data will be passed through and transformed without further developer intervention. 

This modern approach changes and adapts with the speed of data instead of getting bogged down by time-consuming manual processes. Learn more tips and techniques for navigating modern data engineering problems in the StreamSets Community platform.