There are many similarities between data migration and data integration, but they also have key differences. Learn what they are.

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As much as data migration and data integration are considered interchangeable, the two data strategies play very different roles in the data management and preparation lifecycle. It is essential to understand the difference between the two and the unique value they each bring to Big Data. In this guide, you’ll learn about the difference between data migration and data integration, which will help you generate more insights from your most important data.

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What is Data Migration?

Moving data from one location to another is the simple concept behind data migration. It is described as a movement of data from one system to another, characterized by a change of database, application or storage. Data migration can result from a need to modernize databases, build new data warehouses, and/or merge new data from sources, among other reasons.

Key features for data migration

Some of the most important features of data migration tools include:

  • Feature: A tool’s functionality should involve blueprints, task scheduling, workflow organization, data mapping and profiling, ETL tools, and post-migration audits.
  • Manipulation of data sources and target systems: A data migration tool must be compatible with the data source or data type desired by the user.
  • Performance and flexibility: A good data migration tool can transfer data in a short period of time without compromising data quality. Cloud tools offer greater flexibility and scalability than on-premises tools in this area because on-premises tools are subject to hardware parameters.
  • Intuitiveness and ease of use: Intuitive and easy-to-use solutions save users time. Users should consider solutions that are not only intuitive and easy to use, but also backed by exceptional technical support.

Key tools for data migration

Popular data migration tools include:

What is Data Integration?

Data integration refers to the process of merging data from heterogeneous sources into a single data warehouse or database. Data integration is typically implemented to support decision making and improve data quality and analysis.

SEE: Top cloud and app migration tools (TechRepublic)

Large enterprises often use data integration to create data warehouses, which provide users with more powerful reporting, querying, and analysis capabilities. They also present the data in a uniform format.

Key Features for Data Integration

The standard feature set of data integration tools includes:

  • Writing data to target systems: This feature copies data from the source and saves the transformed version to the target applications, services, and systems.
  • Access data from a mix of sources: Data integration allows companies to collect data from various sources and transmit it to target systems in a standardized version.
  • Interaction with sources and targets: Source and target systems can communicate with each other through data integration.
  • Data transformation: Data integration is characterized by the ability to transform data for consumption by a target system.
  • Data flow design: Data integration tools can create data pipelines using various targets, transformations, and sources.

Top Data Integration Tools

Here are examples of data integration tools:

What is the difference between data migration and data integration?


When implementing a new application, data migration takes place only once. As a one-time activity, the initial load contains massive volumes of data. This makes it costly to resolve data issues after migration, which is why it’s crucial to ensure that the migration is fully prepared in advance and handled properly.

On the other hand, data integration is an ongoing process that supports day-to-day business operations. This involves handling incremental data changes. As an ongoing process, data integration is easier to set up and scale over time compared to data migration.


Data integration allows teams to consolidate applications within an organization or combine applications from multiple organizations. It simplifies reporting, analysis and business intelligence, and it contributes to new organizational efficiencies.

Data migration is essential when organizations are looking to upgrade their current systems or completely replace them. It is often performed when there is a need to expand system and storage capacity, move IT services to the cloud, or adopt a centralized database to break down data silos.


Data migration involves the selection, seeding, extraction, transformation, and transfer of data from one system to another. In contrast, data integration combines data from different sources to provide a unified view to users. In the case of data integration, these sources do not always come from other systems, but usually come from various sources that store data differently.

How do data migration and integration work together?

Data migration and integration perform different but vital functions in the management and utility of today’s business applications. They are both centered around data transfer, but they transfer data for entirely different purposes.

Data migration can be used as a foundation for subsequent successful data integration initiatives, as data migration is critical to defining and executing a data quality strategy. It is also characterized by the transfer of existing historical data to a new storage system.

Data migration and integration also work hand in hand in contexts such as cloud data migration. The right cloud onboarding tools can help users accelerate cloud data migration initiatives because the onboarding process provides data professionals with greater visibility, organization, and holistic understanding of the data they have to migrate.

The combination of data integration and migration provides benefits such as converting business insights into actionable insights. The combination also optimizes business processes through increased information exchange between multiple systems. Finally, using data integration and data migration together can increase productivity in an organization, as all data resources are more readily available and the flow of information between different systems is improved.