Today, most businesses are driven by big data, and big data is unstoppable. Speaking of which, data integration and data migration need to be deep-rooted. The process must be smooth, whether data is moving from inputs to a data lake, from a data warehouse to a data mart, from one repository to another, or something else. With an effective data migration strategy, the organization can run over budget and end up with overloaded data processes, or they might discover that their data operations are running below their expectations.
What Is Data Migration?
Data migration is the procedure of shifting data from a source system to a target system. It is a critical task for any data storage expert.
Data migration is essential because it is an imperative component of adding data-intensive applications like data warehouses, databases, data lakes, and huge-scale virtualization projections, boosting or consolidating server and storage hardware.
Data migration may also happen inside systems created on SDD or HDD, or between in-house systems and cloud storage.
Types of Data Migration
1. Migrating Storage
IT moves data during a storage technology refresh. The main aim of tech refreshes is dynamic scaling with enhanced data and management features and quick performance.
2. Migrating Databases
Shifting a database means migrating between platforms, like on-premise to the Cloud, moving the data from one database into a new one.
3. Migrating Application Development
App migration can mean shifting data inside an app, like moving from on-premise MS Office to Office 365 in the Cloud. Moreover, supplanting one app with a different one, like shifting from one accounting software to a new one from another merchant.
4. Migrating to the Cloud
Cloud migration shifts data from on-premise to a cloud, one Cloud to another. Data migration is a separate project that transfers data from the source system to occupy the new one.
Just to make it clear that data migration is different from data conversion and data integration.
Data Migration Challenges & Risks
Data migration is not a simple process and has an image of being risky and challenging. It is time-taking with many planning and implementation steps.
There are compatibility problems in data migration, like modified operating systems and unforeseen file formats, or uncertainty over access rights between target and source systems. Even though the data is formally lost, the organization cannot retrieve it in the target system.
During the data transfer process, data loss can engender. This is not a problem on a small scale, no one might even miss the data, or IT experts can retrieve the files with backup.
But, a mishap is different. In case of a short-term connection failure, the tech experts might be unaware that the short-lived loss suddenly terminated the migration process. The lost data goes unnoticed until an application or a user acknowledges its absence.
Poor performance affects the business:
Several IT departments decide to do a data transfer process in-house to save funds, or the management team makes the decision. However, it is rarely a good option. Migration is a complicated process with significant business implications and needs extensive professional attention.
The Data Migration Success Strategy Process
Even with the risks and challenges, the tech experts can promise a successful project within deadlines and budgets. All it takes is strategic planning, expertise, management buy-in, and tools.
Several IT companies prefer to be interactive, and some migration budgets do not permit for professional advice. But, unless IT already has experts on staff, they will spare money and time by recruiting advisers who are pro at data transfer.
Understand the design needs for data migration, including migration schedules and priorities, capacity planning, backup & replication settings, and prioritizing by data value. At this stage, IT decides on the type of data transfer implementation schedule, also referred to as “big bang or trickle.”
Big bang migration finishes the whole transfer within a restricted time window. Despite some intervals during data processing and movement, the project is completed rapidly.
Trickle migration performs the project in stages, including running sources and target systems parallelly. Trickle migration is more intricate than Big Bang and time-consuming but has less interval time and more testing opportunities.
Work with end-users
Handle the data migration process as a business process and engage your end-users. Work with them and know the data regulations and definitions, what data is subject to acceptance, and priority data that should move initially. Know what they are expecting from the migration- Better performance? Analytics? Or something else.
Audit the data and Fix any problems
Know the TBs of data you are moving, target storage capacity, and growth expectations. Data migration needs auditing the source database for unutilized fields, redundant records, database logic, and alterations before moving data to a new platform.
Moving storage is simpler since you cannot update the older repository and move to a new one. However, data transfer between the storage system to another is not as easy as just copying the data. Utilize software tools to find dark data, and remove or archive them before the transfer.
Backup the source data before moving it
If you lose the data amid the data transfer, be ready to retrieve it to the original system before attempting again. It is always best to create backup images to instantly retrieve the original system if the migration loses the data.
Shift and verify the data
Invest in automated data transfer that enables you to schedule unsteady data migration subsets, verifies data integrity in the target system, and report troubleshooting and validation problems.
Final Test and Shutdown
After migrating all the data, test the migration with a mirror of the production environment. After it checks out, meticulously go live and perform final tests. Shutdown the legacy system once the new environment is running seamlessly.
Instead of spending costly resources to upgrade the source data before the transfer, regulate governance controls, and analytics in new conditions. Continually monitor the transferred data for abandoned work sets, strange access patterns, and security. The data migration will conduct better in its new platform, and the next transfer will take place smoother and faster.
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