Data Integration (Design Patterns)

To make our critical enterprise data more usable and to increase its availability faster, developers can use data integration patterns to standardize the integration process. There are five data integration patterns based on business use cases.

#1: Data Migration

The concept of Migration is simply the act of moving data from one system to another. Data Migration scenario requires the following; a source system where the required data resides prior to execution, a definitive criteria which will be used to determine the scope of the data to be migrated, a transformation that the data must pass through, a destination system where the revised data will be inserted, and a logging or auditing ability to capture the results of the migration to track final state vs desired state of our data.

Value of Data Migration

Simply put, data migration is an essential element of all of our data systems. We spend a lot of time and effort creating and maintaining critical enterprise data, and data migration is key to keeping that data agnostic and allowing a neutral view from the tools used to create, view, and manage it. Without migration, we would lose all the data that we have amassed any time that we want to change tools — crippling our ability to be productive in the digital world.

Usefulness of Data Migration

Data migration occurs when moving data from one system to another, moving data to another system or newer instance of our system, spinning up a new system that extends your current infrastructure, backing up a dataset, adding nodes to database clusters, replacing database hardware, consolidating systems and many more.