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Data migration & Backup

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Data migration is the process of moving data from one location to another, one format to another, or one application to another. Generally, this is the result of introducing a new system or location for the data. The business driver is usually an application migration or consolidation in which legacy systems are replaced or augmented by new applications that will share the same data set.

There are numerous business advantages to upgrading systems or extending a data center into the cloud. For many firms, this is a very natural evolution. Companies using cloud are hoping that they can focus their staff on business priorities, fuel top-line growth, increase agility, reduce capital expenses, and pay for only what they need on demand. However, the type of migration undertaken will determine how much IT staff time can be freed to work on other projects.

First, let’s define the types of migration:

  • Storage migration. The process of moving data off existing arrays into more modern ones that enable other systems to access it. Offers significantly faster performance and more cost-effective scaling while enabling expected data management features such as cloning, snapshots, and backup and disaster recovery.
  • Cloud migration. The process of moving data, application, or other business elements from either an on-premises data center to a cloud or from one cloud to another. In many cases, it also entails a storage migration.
  • Application migration. The process of moving an application program from one environment to another. May include moving the entire application from an on-premises IT center to a cloud, moving between clouds, or simply moving the application's underlying data to a new form of the application hosted by a software provider.

Data migration involves 3 basic steps:

  1. Extract data
  2. Transform data
  3. Load data

Moving important or sensitive data and decommissioning legacy systems can put stakeholders on edge. Having a solid plan is a must; however, you don’t have to reinvent the wheel. You can find numerous sample data migration plans and checklists on the web.

  • Premigration planning. Evaluate the data being moved for stability.
  • Project initiation. Identify and brief key stakeholders.
  • Landscape analysis. Establish a robust data quality rules management process and brief the business on the goals of the project, including shutting down legacy systems.
  • Solution design. Determine what data to move, and the quality of that data before and after the move.
  • Build & test. Code the migration logic and test the migration with a mirror of the production environment.
  • Execute & validate. Demonstrate that the migration has complied with requirements and that the data moved is viable for business use.
  • Decommission & monitor. Shut down and dispose of old systems.

To move applications and data to more advantageous environments, Gartner recommends "disentangling" data and applications as a means of overcoming data gravity. By making time at the beginning of the project to sort out data and application complexities, firms can improve their data management, enable application mobility, and improve data governance.

The main issue is that every application complicates data management by introducing elements of application logic into the data management tier, and each one is indifferent to the next data use case. Business processes use data in isolation and then output their own formats, leaving integration for the next process. Therefore, application design, data architecture, and business processes must all respond to each other, but often one of these groups is unable or unwilling to change. This forces application administrators to sidestep ideal and simple workflows, resulting in suboptimal designs. And, although the workaround may have been necessary at the time, this technical debt must eventually be addressed during data migration or integration projects.