When business owners think about modernizing a legacy application, they often focus on the most visible part: a sleek new user interface. However, the real challenge often lies beneath the surface. It’s data migration strategy. Moving data from an outdated system isn’t just a simple copy-paste job. It requires deep planning and expert execution. While automated data migration tools promise speed and cost savings, they are not a silver bullet. In this article, we’ll explore why automated tools alone aren’t enough, when developer expertise remains irreplaceable, and how a hybrid approach can save time and money.

How Databases Evolve During Legacy Software Modernization

When we talk about how data usually changes in the context of modernizing legacy software, we typically mean the following key processes.

Schema Migration includes incremental changes to the database structure itself, such as:

  • Adding, modifying, or dropping tables, columns, indexes, or constraints;
  • Adjusting data types or relationships between tables;
  • Essentially, this is about evolving the database design while keeping the same underlying system.

Data Migration as a process of moving data from one database system to another, such as:

  • Extracting data from an old database;
  • Transfering to a different DBMS (database management system), a new version of the same DBMS, migrating from on-premise to cloud database, etc.

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A Real-World Challenge: From One Table to Multiple Tables

Legacy system modernization often means completely restructuring how data is stored. Let’s consider a real-world example of legacy system migration. Take a 20-year-old system, for example, where all user information was crammed into a single users table, which is pretty typical for old software. We need to deliver a new software version, and the new system usually provides much more detailed functionality. Therefore, that same data might be spread across multiple normalized tables, with relationships, constraints, and additional fields that didn’t exist before.

For example, In a legacy application, user data can be stored in a single table called users, which includes the following fields:

  • id
  • email
  • nickname
  • username
  • Several fixed columns representing the user’s interests (e.g., interest_1, interest_2, interest_3)

This structure is common in older applications that treated interests as fixed, hardcoded fields in the main user table. Such a table can be useful for e-commerce and marketplace platforms, for example to recommend products, collections, or promotions. A user who marks “home decor” and “kitchen gadgets” as interests may receive targeted recommendations and offers. This model is also common in eLearning platforms and EdTech apps to recommend relevant courses or learning materials based on interests. When user interested in “machine learning” and “data visualization” will see courses, webinars, or learning paths tailored to those topics.

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Why Dividing User-related Data Into Multiple Tables

Dividing user-related data into multiple tables, such as separating interests, profile data, and relationships, introduces structural and functional advantages to a modern application. This approach follows the principles of relational database normalization, and it offers both technical benefits and business value. Here’s what it enables:

  • Scalability and Flexibility. Instead of being limited to a fixed number of interest fields (e.g., interest_1, interest_2), a separate interests table allows for unlimited, dynamically added interests. Proper data migration strategy helps support future features, like tagging, user-generated topics, or hierarchical categorization, without redesigning the schema;
  • Efficient Queries and Personalization.  With a proper schema, you can quickly query for all users interested in a specific topic or all interests of a given user. It enables fast personalization features like interest-based recommendations, user segmentation, and content filtering;
  • Localization and Internationalization. With a separate interests table, it’s easy to localize interest names into different languages;
  • Better Maintainability. Adding a new feature (e.g., allowing users to follow interests or subscribe to updates) only requires changes in the interests or user_interests tables;
  • Rich Metadata and Behavior Modeling. You can add attributes to interests, like popularity, category, or trendiness, or track user behavior (e.g., last_engaged_at). This feature improves user experience through trend-based suggestions and behavioral analytics.

Modernized Schema

To reap the described benefits, In the new system, user interests can be treated as a flexible, scalable relationship:

  1. A separate interests table holds all possible interest categories;
  2. A junction table user_interests maps users to their selected interests.

Therefore, in a modernized app, we’ll have three tables instead of a single users table from the outdated app. Additionally, the new system no longer stores a single username field. Instead, it splits this into:

  • first_name
  • last_name
  • gender (to support features like salutation: Mr., Ms., in an AI chatbot, for instance)

However, the original system does not store gender. Therefore, it must be inferred based on the user’s name if no other information is available.

The Cost of Fully Manual Data Migration

The process of data migration is often far more labor-intensive than expected. We’ve only described a few basic features, and even implementing this little set requires splitting a single legacy table into three normalized tables. In real-world scenarios, the number of such transformations is often significantly higher.

Additionally, consider the volume of data handled by applications that have been on the market for decades. Migrating such data structures is a major task. The amount of custom logic a developer must implement to ensure data integrity and correct representation can be substantial.

This naturally raises the question:

Is there a way to reduce the workload, cut modernization costs, and accelerate the release of the new software version?

The answer is yes, and this is exactly where automated data migration tools come into play.

How Hybrid Data Migration Help Developers Modernize Legacy Systems

How Automated Tools Help Migrate Data 

While complex transformations, such as splitting one legacy table into multiple modern relational tables, require custom logic and developer-written scripts, automated data migration tools can still significantly accelerate the overall process when applied strategically. Here are some examples:

  1. Schema Discovery and Mapping. Automated data migration tools, such as Talend, can inspect and map source schemas to target schemas, highlighting structural differences. This is especially useful during initial planning, since it helps developers understand which parts of the schema need custom transformation and which can be migrated directly;
  2. Bulk Data Transfer. Once transformation logic is implemented, automated tools, such as Fivetran, can execute bulk data loads to transfer millions of records reliably and with built-in error handling, logging, and retry mechanisms. This reduces human error and dramatically shortens the time required to move large data sets;
  3. Validation and Monitoring. Automated systems like Datafold can verify that row counts and data types are consistent between source and target. Such data validation tools free developers from writing boilerplate verification logic and allows them to focus on business-critical transformation code.

By offloading tasks like extraction, loading, and validation, automated tools can save developers hours or even days on each test iteration of the migration process. For business owners modernizing decades-old software, this hybrid approach offers:

  • Lower development costs, as automation reduces the number of custom scripts required;
  • Faster iteration, making it easier to test, refine, and re-run migrations before final deployment;
  • Reduced risk, thanks to automated validation and rollback capabilities.
Ready to streamline your legacy system migration? Let our experts help you combine automation with the right development strategy to reduce costs, accelerate timelines, and ensure data integrity
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How Developers Handle Tasks That Are Beyond the Capabilities of Automated Tools

Automated data migration tools can help developers migrate to a different database management systems or to a new version of the DBMS in use, applying the required data manipulations to ensure accurate representation. Also, they can copy the id, email, and nickname fields with little trouble. Possibly, there will be no issues with replicating the old users table into a staging environment.

Automated data migration tools can’t successfully perform tasks required for the use case we described earlier. For instance, infer gender from names (e.g., determine “Sarah” is female, “John” is male), or populate the interests table dynamically from user-provided values. Also, there could be issues with deduplicating shared interests across users (e.g., don’t insert “kitchen gadgets” twice) or creating the correct many-to-many relationships in user_interests.

To address these limitations and handle the data migration process, developers must first copy to a new system all transferable data “as-is,” without any changes. This can be done using SQL queries or automated migration tools. Then, for data requiring additional processing, developers write custom migration scripts that:

  1. Iterates through each row in the legacy users table;
  2. Splits the username into first and last names;
  3. Uses predefined logic or an external service to infer gender based on the first name;
  4. Normalizes user interests. It includes adding new interests to the interests table (if not already present) and adding corresponding entries in user_interests linking users to their interests.

Example:

  • User 1: Sarah Jones → first_name = Sarah, inferred gender = Female
    • Interests: A, B, C → Insert A, B, C into interests (if new), and link Sarah to them in user_interests
  • User 2: John Chivers → first_name = John, inferred gender = Male
    • Interests: B, C, D → B and C already exist; insert D; create mappings for all three in user_interests

Read Also From Old to Gold: Transforming Legacy Systems with Modernization Techniques (with Real Examples)

The Human-Automation Partnership in Data Migration

While automated tools bring undeniable speed and reliability to repetitive migration tasks, they cannot fully replace the insight, logic, and domain expertise of experienced developers. Effective data migration requires a hybrid approach. Automation can handle the heavy lifting (bulk transfers, change tracking, and validation) while developers focus on high-value tasks like transforming data models, preserving business logic, and ensuring consistency across evolving schemas.

This partnership between tooling and talent reduces cost, minimizes risk, and ensures long-term maintainability of the new system. At XB Software, we combine cutting-edge tools with deep technical expertise to modernize legacy systems efficiently. Ready to migrate smarter? Contact us to discuss how our hybrid approach can transform your legacy data into a modern asset.