What Do You Mean By Data Migration?
Data migration is the process of transporting data between computers, storage devices, or formats. It is a key consideration for any system implementation, upgrade, or consolidation…
All Data Migrations Are Not The Same
The usual types of data migration are storage, database, application, cloud, and business process migration.
We migrate the data during a storage technology refresh. The goal of a technology refresh is faster performance and dynamic scaling along with improved data management features.
Migrating a database can mean moving between platforms, such as on-premise to the cloud, or migrating the data from one database into a new one.
Application migration can mean moving data within an application, such as shifting from on-premises MS Office to Office 365 in the cloud. It can also mean replacing one application with a different one, such as moving from one accounting software to a new accounting platform from a different vendor.
Migrating to the Cloud
Cloud migration moves data from on-premises to a cloud, or from one cloud to another. This type of data movement is not the same as backing up to the cloud data migration is a distinct project that moves data from the source environment to populate the new one.
Moving data between storage devices, locations, or systems. It includes subsets like quality assurance, cleansing, validation, and profiling.
Transforms data from a legacy application to an updated or new application. The process is ETL: extract, transform, load.
Combines stored data residing in different systems to create a unified view and global analytics.
Data Migration Risks!
Data migration has the reputation of being risky and difficult. It’s certainly not an easy process. It is time-consuming with many planning and implementation steps, and there is always some risk involved in projects of a large magnitude.
During the data migration process, data loss can occur. On a small scale, this may not be a problem – no one may ever miss the data, or we can restore files with backup. However, catastrophic data loss is different. In the case of a short-term connection failure, IT may not even know that the short-lived failure abruptly terminated the migration process. The missing data goes unnoticed until a user or application calls for it and it’s not there.
Planning our Strategy
Understand the design requirements for migrated data including migration schedules and priorities, backup and replication settings, capacity planning, and prioritizing by data value. This is also the stage where we decide on the type of migration implementation schedule, a “big bang or trickle.” See below to learn more about implementation schedules.
Big Bang migration completes the full transfer within a limited time window. There is some downtime during data processing and movement, but the project is completed quickly.
Trickle migration carries out the project in phases, including running source and target systems in parallel. Trickle migration is more complex than Big Bang and take longer but has less downtime and more testing opportunities.
Once we have migrated all data, we test the migration using a mirror of the production environment. When it all checks out, we can take your information live and conduct final tests. Once the new environment is running smoothly, we will shut down the legacy system and/or program.