These days, moving information from one place to another is nothing more than a simple copy-and-paste operation. Everything becomes much more complicated when it comes to transferring millions of data to a new system. However, several companies treat even a massive data migration as a simple, low-level task, but it results in spending more time and money. Recent studies revealed that a considerable rate of data migration projects went over budget and yet another was more difficult than expected or even failed. How do you avoid falling into a failed project? The answer lies in understanding the essential elements of the data migration process, from its vital parts to its completion.
If you are already familiar with the theoretical aspects of a migration, you can move on to the phase of the data migration process where there are practical recommendations.
In general terms a data migration is the transfer of existing historical data to a new data storage and processing system or file format. This process is not as trivial as it may seem. It involves a lot of preparation and post-migration activities, including planning, backup, quality testing and validation of the results.
Data migration comes as part of a larger project such as modernization or replacement of legacy software, expansion of system and storage capabilities, introduction of an additional system to work alongside the existing application, moving to a centralized database to eliminate data silos and achieve interoperability, moving IT infrastructure to the cloud, or merging activities, when IT environments need to be consolidated into a single system.
There are commonly used types of data migration, however, a particular case of data transfer may pertain, for example, to migration of both the database and the cloud or involve migration of the application and the database at the same time.
Database migration: A database is not just a place to store data. It provides a structure for organizing information in a specific way and is usually controlled through a database management system (DBMS). So most of the time, database migration means an upgrade to the latest DBMS version (so-called homogeneous migration), a switch to a new DBMS from a different vendor, e.g. from SQL to PostgreSQL or from Oracle to MSSQL (so-called heterogeneous migration).
Application migration: When a company changes enterprise software vendors, for example, a real estate company implements a new property management system or a hospital replaces its legacy CRM system, this requires moving data from one computing environment to another. The key challenge here is that old and new platforms may have unique data models and work with different data formats.
Storage migration: Storage migration occurs when a company acquires modern technologies and discards obsolete equipment. This involves transporting data from one physical medium to another or from a physical environment to a virtual one.
Data center migration: A data center is a physical infrastructure used by organizations to maintain their critical applications and data. Put more precisely, it is the very dark room with servers, networks, switches and other IT equipment.
Cloud migration: Cloud migration is a popular term that encompasses all the cases mentioned above, whether they involve moving data from on-premises to the cloud or between different cloud environments.
Different approaches to data migration
Choosing the right approach to migration is the first step in ensuring that the project runs smoothly, without serious delays.
Big Bang Scenario.
Some advantages could be considered as less costly, less complex, takes less time, all changes occur once. Oppositely: high risk of costly failure, requires downtime. Also, it moves all data assets from the source to the target environment in a single operation, within a relatively short time window.
This approach is suited to small enterprises or businesses working with small amounts of data. It does not work for mission-critical applications that must be available 24/7.
Wave data migration
This approach is less prone to unexpected failures, zero downtime required.
However, it is more expensive, takes longer, requires additional effort and resources to keep two systems up and running; this approach brings Agile expertise to data transfer. It breaks down the entire process into cycles, each with its own objectives, timelines, scope and quality controls.
Slow migration involves: running the old and new systems in parallel and transferring data in small increments. As a result, you benefit from zero downtime and your customers are happy due to 24/7 application availability.
It is important to consider that the iterative approach takes much longer and adds complexity to the project. Your migration team must track what data has already been transported and ensure that users can switch between two systems to access the required information. Another way to perform a slow migration is to keep the legacy application fully operational until the end of the migration. As a result, your customers will use the old system as usual and switch to the new application only when all data is loaded correctly in the target environment.
However, this scenario does not make things easy for your engineers. They must ensure that data is synchronized in real time between two platforms once it is created or modified. In other words, any change in the source system must trigger updates in the target system.
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