Big Bang vs Trickle: Data Migration Approaches Head-to-Head

Szabolcs Kun
Szabolcs Kun
07 Aug 2023 · 4 min read

We have explored the lifecycles of cloud migration in a previous post, but it is also important to select a migration approach that suits the needs of the organization. The migration approach is about how exactly a data asset is moved from the source system to the target platform. The two main options available to us are called the Big Bang Migration and the Trickle Migration.

Let’s take a closer look and also compare the two.

What Is the Big Bang Migration?

The Big Bang migration approach involves transitioning all system components from the existing infrastructure to the new platform in a single, coordinated event. This method requires meticulous planning, as it requires a complete system shutdown to implement the migration, followed by the simultaneous launch of all migrated components on the new platform. A point-in-time copy of the data is migrated to the new environment.



Advantages and Risks of the Big Bang Migration 

The advantages of this approach include:

  • a clear demarcation between the old and new systems
  • reduced coexistence complexities
  • the potential for faster realization of benefits from the new platform

However, the risks associated with this method are higher due to the need for comprehensive testing and validation before the switch, as well as potential difficulties in reverting to the old system in case of issues.

What Is the Trickle Migration?

The Trickle migration approach, also known as phased or incremental migration, entails transitioning system components from the existing infrastructure to the new platform in gradual, manageable stages. This method allows for a smoother transition, as it allows the old and new systems to coexist for a certain period, enabling organizations to fine-tune the new platform and resolve any issues before fully committing to it. 

In cases where both the original data storage system and the new data storage solution are running simultaneously, it may be necessary to implement a synchronization logic to maintain data integrity and avoid any inconsistencies between the two systems. 

Developing an efficient synchronization logic can be challenging, and it is crucial to carry out extensive testing to guarantee flawless data consistency across both systems throughout the migration process. Once the migration is successfully completed and the switchover is finalized, synchronization can be deactivated.



Advantages and Risks of the Trickle Migration

The main pros of the Trickle approach are:

  • reduced risk, as issues can be detected and resolved during the incremental migration
  • the ability to maintain business continuity throughout the process

However, this method can be more complex due to the need for coexistence management, and it may take longer to realize the full benefits of the new platform.

How to Choose a Data Migration Approach

Choose Big Bang Migration When…Choose Trickle Migration When…
the majority of data flows involve batch processingbatch processing execution windows are short (micro-batch based)
stream processing windows are relatively long (e.g., more than one hour)stream processing occurs in real-time
delayed processing is considered acceptabledelayed processing is unacceptable
downstream systems do not rely on the database for enriching real-time processingdownstream systems utilize the database for enhancing real-time stream processing
automated migration, testing and validation are requireddata comparison testing (regression testing) is enough
Big Bang vs. Trickle: Suitability Comparison Table

It is crucial to emphasize that incremental migrations can involve either a step-by-step transition between systems or a gradual switch between individual data workflows. 

The level of abstraction to be applied can be chosen accordingly. In cases where a system primarily handles batch processing with only a few workflows dedicated to near real-time stream processing, Big Bang migration can be employed for the batch workflows, while the Trickle approach can be used for the more sensitive, business-critical real-time workflows. This ensures a tailored migration strategy that caters to the specific requirements of each workflow within the system.

When choosing between the Big Bang and Trickle migration approaches, decision-makers must carefully consider several factors, including their organization’s risk tolerance, available resources, and the complexity of the migration.

The Big Bang approach may suit organizations with a high-risk tolerance, sufficient resources for thorough planning and testing, and a desire for a rapid transition to the new platform.

On the other hand, the Trickle approach may be more appropriate for organizations seeking to minimize risk, maintain business continuity, and incrementally refine the new platform while phasing out the old system.

Parting Thoughts

Ultimately, choosing between the Big Bang and Trickle migration approaches depends on each organization’s unique needs and priorities. By understanding the advantages and disadvantages of each method, decision-makers can select the most suitable option for their data warehousing and big data solution migrations. If you would like more specialized guidance on how to make your migration project a success or which approach and migration strategy to use, get in touch with the DATAPAO team today.