There is 4 different methods to migrate data.
Exporting and Importing: When you export an import, you export the data in a neutral format, such as CSV (comma-separated value) files, and then edit the files so that they are formatted before being imported into the target database. This is a slow method of data migration because the work of changing data types and structures must be done manually by a data scientist.
Scripting: In this case, you write the script to convert the data to the appropriate format for the target data warehouse or store. It is faster than the process of exporting and importing manually, but it is still a very laborious process because you have to write a different script for each source and purpose.
Using a traditional tool: You can use a third-party tools to move data. These tools are designed to capture, modify, and load data efficiently. These tools are built to load data from multiple sources into different targets and they are designed to process large amounts of data. However, not all tools are built equally. Most data needs to be moved in batches and is primarily designed for relational databases, Many data resources these days are not structured. Additionally, they often have very strict rules and expectations about the data, forcing you to re-process all your data if anything changes in the schema, source, or goal.
Using a modern tool: Advanced tools such as Pilog MDRM can process data in real-time rather than in batches. They are ideal for managing a wide variety of resources and goals and can modify mappings on the fly to suit your company's needs. Additionally, they can be scaled up or down depending on the changing output.