Climbing the Digital Mountain — The Data Migration Challenge

A hot topic in today’s digital landscape is data migration. For those unfamiliar with the term, it basically is when you need to move data from one data source or system to another. There are many reasons why data may be migrated from one system to another, these reasons may include:

• Legacy system decommissioning
• System upgrade
• Acquisitions or mergers
• Data replication or backups
• Cloud adoption

Regardless of the reason, data migration can come with significant challenges.

This article will cover some migration challenges, data migration strategies and how data migration can be facilitated and be made more manageable by using the right platform.

Data Migration Challenges

Some challenges posed by data migration may include:

Data Quality and Integrity

Ensuring the accuracy, completeness, and integrity of data during migration is crucial. Inaccurate or incomplete data can lead to errors and impact business operations.

Downtime and Disruption

Minimizing downtime during migration is challenging, especially in scenarios where systems need to be taken offline. Any disruption to operations can affect productivity and customer satisfaction.

Data Mapping and Transformation

Mapping data between different systems and transforming it to meet the target system’s requirements can be complex. Inconsistencies in data structures may require thorough analysis and mapping strategies.

Data Volume and Scalability:

Managing large volumes of data can strain resources and impact performance during migration. Ensuring scalability and efficiency become critical, especially in big data scenarios.

Legacy System Compatibility

Migrating from legacy systems to modern ones may involve dealing with outdated technologies and formats. Ensuring compatibility and seamless integration can be challenging.

Data Consistency and Validation:

Maintaining data consistency across multiple systems and ensuring that data is valid and accurate after migration requires careful planning and validation processes.

Resource Constraints

Insufficient resources, both in terms of manpower and technology, can hinder the migration process. Adequate planning and resource allocation are essential for a successful migration.

Customization and Complexity

Customized data structures and complex business logic can add layers of complexity to migration. Ensuring that the migration process accommodates these complexities is a significant challenge.

When choosing a strategy, you will need to keep these constraints and challenges in mind.

Data Migration Strategies

There are several approaches to data migration, each with its own advantages and challenges. Here are some common data migration approaches:

Big Bang Migration:

• Description: In this approach, the entire system is migrated in a single, comprehensive process.
• Advantages: Simplicity and speed.
• Challenges: High risk as any issues can affect the entire system at once.

Phased Migration:

• Description: The migration is divided into multiple phases, and each phase is implemented separately.
• Advantages: Reduced risk, easier to manage, and allows for testing and adjustment between phases.
• Challenges: Requires careful planning and coordination.

Parallel Adoption:

• Description: Both the old and new systems run simultaneously during the migration process.
• Advantages: Minimal disruption, easy fallback if issues arise.
• Challenges: Increased complexity and resource requirements.

Pilot Migration:

• Description: A small portion of the data is migrated first, and if successful, the migration is expanded to the entire dataset.
• Advantages: Lower risk allows for testing and validation before full-scale migration.
• Challenges: Potential difficulties in scaling up the migration process.

Hybrid Migration:

• Description: Combines different migration approaches based on the specific needs of different parts of the system or data.
• Advantages: Flexibility to address diverse requirements.
• Challenges: Requires careful planning and coordination.

Strangler Pattern:

• Description: New functionality is gradually introduced, and the old system is gradually replaced over time.
• Advantages: Low risk, allows for iterative improvements.
• Challenges: May require ongoing support for both old and new systems during the transition.

Trickle Migration:

• Description: Data is migrated incrementally over an extended period, typically in the background.
• Advantages: Low impact on ongoing operations, continuous data consistency.
• Challenges: Longer migration duration.

Data Synchronization:

• Description: Data is synchronized between the old and new systems until the migration is complete.
• Advantages: Ensures data consistency during the transition.
• Challenges: Requires robust synchronization mechanisms.

Regardless of what strategy you choose, it is imperative that you choose the right tools for the job. These tools can make the process much smoother and much more manageable.

Data Migration via tools

As with any software, you need to decide on what technology to build your process and application with. You can opt to develop your migration application with traditional code, however another good option is to do it with a low-code tool. Low-code tools have a variety of benefits such as implementation speed, lower skill requirements, quicker time to live and simplified infrastructure. You can capitalize on these benefits even more when choosing a platform that specializes in data integration and migration.

For example, you can transfer data from any source to any target by using Linx. In Linx you can build processes that will pull data from: files, APIs, Databases and queues. This data can then be sent to any target. Extracting data from one database to be loaded onto another can be accomplished by using the database plugin.

The below process illustrates how you can migrate data from one database to another:

1. A bulk copy function is used to write all the data quickly and efficiently
2. The data is pulled from the old database via an ExecuteSQL function
3. Data is added to the Loader. You can change specific values or apply transformations or business rules before adding the data to the loader.

Alternatively, you can read data from one database and add it to another using the ExecuteSQL component. You can apply transformations and checks on values on a record-by-record basis. Specific values can also be selected to be added to specific tables, which is especially useful when the schemas of the databases are different.

When combined with the scheduling functionality (timers and crons) you can create an automated data migration system or application that will apply custom logic and transformations on your data based on your requirements.

There are other tools that can give you a similar result, I chose Linx because I am very familiar with it and because I believe that it offers a high degree of flexibility which is beneficial for complex migrations may. Regardless of what tool you choose, a low-code tool can greatly benefit your migration process.