Data migration strategies

Even though from some points of view data migration seems problematical and difficult to apply, no one actually doubts in its necessity. More or less regularly almost all companies which operate on data have to update their systems, applications, platforms. It's a natural situation which originates from the fact that not only computer systems are changing, but also so is business. What it means is the fact that a need for data migration might not only be a consequence of data storage system becoming outdated, but also the result of changing business condition. Ensuring the best data quality, also through efficient data migration, is crucial to responsible management in 21st century world.
Data migration, being a method for letting data originated from one source be compatible with another which it's going to be loaded into, is simple only at first sight. The deeper one knows the problem, the more questions he has, beginning with the most important one - how to make company suffer least because of data migration. In fact, it depends on chosen strategy. Basically, there are two different strategies, two different approaches to data migration. And they differ mainly in the way migration is proceeded.

Big bang migration

The first strategy, called big bang migration, is someway uncompromising. In a word, it suggests shutting all applications and databases immediately, stopping the work and putting all force in data migration. In fact, it really seems to be a good option, because only this way guarantees that the migration lasts as short as possible. Moreover, it almost eliminates the risk that something unpredicted happens during the process. On the other hand though big bang migration might be destructive to organization's work. Especially in cases of companies which depend on data in a real time, being cut from data might be a real problem.
There is, of course, a way to minimize the negative influence of big bang migrations. In most companies which decide to choose this type of data migration strategy, the process is being initialized after work hours or on holidays. This way, cutting off the access to data may cause the least problems.

Big bang migrations

In favourAgainst
  • short time of migration
  • can be run during weekends, holidays, etc.
  • obligatory organization systems' downtime
  • risky

  • Trickle migration

    In fact, the work within most companies last 24 hours long, during weekends and public holidays as well (even if employees actually have a day off, systems can't enjoy a break). Thereupon, managers often cannot afford to turn off the systems even during holidays. However, data migration still has to be run anyway. Fortunately, there is an option to migrate data without a need to shut the whole system. It's called trickle migration and is performed during the normal work of all involved systems. How is it possible then?
    The idea behind trickle data migration is not to shut the whole system at once, but operate only on its chosen areas so that all other could be accessible at the moment of migration. This way, employees keep continuous access to data, even though migration might last even 24/7.

    Trickle migrations

    In favourAgainst
  • no interruption in employees' work
  • no system downtime
  • long time of migration
  • more complex to organize

  • Summary

    Basically, there are two different approaches to data migration. One is reflected by big bang migrations which means migrating data as fast as possible but within the obligatory system downtime, and another are trickle migrations which are significantly slower, but can be held in parallel with regular work of company employees and systems.
    Properly prepared data migration consists of a batch of processes, preceded with deep analysis of needs and requirements. Migration plan, concretized during the preparation, allows not only to understand what has to be done, but also to verify if everything is the way it was meant to be in the end. Following the eight steps listed above is a good base for ensuring that data migration will run properly, but it's good to remember that every case is different and sometimes might require a little bit different treatment.