What are the Strategies for Data Migration in DBMS?

Data Migration means moving digital data. It is the process of transferring data to a different location, file type, media, storage system, database or application. Almost every company regularly migrates data due to system upgrades, increased data volume, improved system performance, and business process changes.

Why is Data Migration Strategy Important?

A data migration strategy is important because it minimizes costs and the fear of losing the data. It helps to reduce system downtime. Also, it improves the user experience of the deployed data.

Important Steps in the Data Migration Process

• The first step of data migration is to set the objective and requirements gathering. Now plan your data migration based on the scope.
• The Next step is you need to assess the source and target data migration system.
• Now check on potential risks and obstacles associated with the data migration process.
• Another important step is you need to determine the required human, financial, and technical resources to execute the data migration.
• Choosing the appropriate tools and technologies for extracting, transforming, and migrating the data is another major step.
• After migrating the data, you must do proper testing to ensure the accuracy and consistency of the migrated data.
• Finally, create proper documentation of the end-to-end migration process and share knowledge transfer to relevant members.

Data Migration Strategies

These are the 4 major data migration strategies.

• Big Bang Data Migration
• Incremental Data Migration
• Hybrid Data Migration
• Parallel Data Migration

1. Big Bang Data Migration

Big Bang migration is the process of moving applications and database (DB) data from the old system to the new system at a specific point in time. It is a single-step process.

It can be achieved by the below steps:

• To begin with, analyze the system data samples and plan the data migration based on the analysis. You can recommend the new architecture and estimate the cost of the migration and timelines.
• At this stage, you can implement the recommended architecture and test the already analyzed data samples. Also, check the selected migration tool details and working methodology. This stage generally takes more time because you should gather entire system details. Now pre-migration steps are over.
• Now migrate all the data at once from the source to the new system. So system downtime is minimal.
• Let users verify the migration result. If the data migrated properly, turn off the source system.

Example: Let’s say sports content has been stored in a private location (Oracle DB) for many years and they decide to transfer all data to a new database (cloud-based Microsoft SQL Server DB).

2. Incremental Data Migration

An incremental migration strategy supports gradually transitioning from an old system to a new system or new DB. In this process, Migrate the users or system features phase by phase instead of all at a time.

Example: Consider a large e-commerce company that wishes to migrate customers information (such as purchase details, preferences, and finance details) from one DB to another without affecting the customer’s business.

3. Hybrid Data Migration

A hybrid data migration strategy combines two or more migration methods to achieve the specific needs of the organization’s data migration.

Example: This type of migration is mainly designed for military systems that have sensitive data.

4. Parallel Data Migration

In the Parallel Data Migration strategy, both old and new systems run simultaneously while data is imported into the new system. When all issues are resolved and the new system is working smoothly, it will replace the old system.

This approach contains the following steps:

• Planning: This step involves determining migration goals and gathering resources for old and new systems.
• Data synchronization: A data synchronization mechanism must be established between the old and the new system.
• Parallel work: Make sure the old and the new systems run simultaneously.
• Monitoring: Regular monitoring of data consistency and system performance.

Example: Suppose a large financial institution upgrades its banking system to a new DB. During the migration, both the old and the new DB run parallelly.

Comparative Analysis of Data Migration Methods

Migration Methods	Advantages	Disadvantages
Big Bang Migration	It is a single-step process, so it can be done quickly. It is simple because you concentrate on a single migration event.	Risk is high because if any issue happens during the migration, the entire system will be affected. It is difficult to handle all the data at once.
Incremental Migration	It gives a chance to scale the migration efforts from smaller to larger data sets. Risk is low because if any issue occurs, you can simply check and fix the recent phase migration.	It takes longer duration because of phase-by-phase migration. Too much of phases lead to increasing resource usage.
Hybrid Migration	It provides more flexibility. You can choose the migration methods based on the requirement. This method allows to optimal use of resources.	It is a time-consuming method than single-step migration. Managing multiple migration methods simultaneously increases the complexity.
Parallel Migration	Risk is low because in case of data loss or system failure, the old system running continuously (as a fallback) It supports smooth transition due to the fallback system(the old system acts as a fallback).	It is costly because of additional resources and hardware. This method is complex because needs to maintain data synchronizing.

Frequently Asked Questions on Data Migration in DBMS – FAQs

Which data migration is a quick process?

Generally, Big Bang migration can be completed quickly. Because data migrated from the old system to the new system at a specific point in time. It is a single-step process.

Which data migration is suitable to move data to the new system without affecting the old system?

The best choice for migrating the data to the new system without affecting the old system is Parallel Data Migration. In this strategy, both old and new systems run simultaneously while data is imported into the new system. So it supports the seamless transition.