Person looking at a visualization of an interconnected big data structure.
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As much as data migration and data integration are understood as interchangeable, the two data strategies play very different roles in the data management and preparation lifecycle. It is critical to understand the difference between the two and the unique value they each bring to big data. In this guide, you will learn more 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 shift of data from one system to another, characterized by a change in database, application or storage. Data migration may 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:

  • Functionality: The functionality of a tool should involve plans, scheduling jobs, organizing workflows, data mapping and profiling, ETL tools and post-migration audits.
  • Handling of data sources and target systems: A data migration tool should be compatible with a user’s desired data source or data type.
  • Performance and flexibility: A good data migration tool can transfer data in a short time frame without compromising data quality. Cloud tools offer greater flexibility and scalability than on-premises tools in this area, as on-premises tools are subject to hardware parameters.
  • Intuitiveness and ease of use: Intuitive and easy-to-use solutions save time for users. Users should consider solutions that are not only intuitive and easy to use but also backed up with 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 usually implemented to support decision-making and better data quality and data analysis.

SEE: Top cloud and application migration tools (TechRepublic)

Large enterprises often use data integration to create data warehouses, which offer users more powerful reporting, querying and analytics capabilities. They also present 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 target applications, services and systems.
  • Accessing data from a mix of sources: Data integration enables enterprises to collect data from diverse sources and deliver 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 to be consumed by a target system.
  • Designing dataflows: Data integration tools can create data pipelines using various targets, transformations and sources.

Key tools for data integration

Examples of data integration tools include: 

What is the difference between data migration and data integration?


When implementing a new application, data migration happens once. As a one-off activity, the initial load contains massive data volumes. This makes it expensive to fix data issues after the migration, which is why it’s crucial to ensure migration is fully prepared for in advance and handled correctly.

On the other hand, data integration is a continuous process that supports the daily operations of the business. It involves managing incremental changes to data. As a continuous process, data integration is easier to put in place and change over time as compared to data migration.


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

Data migration is key when organizations seek to upgrade their current systems or replace them altogether. 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 tear down data silos.


Data migration involves selecting, priming, extracting, transforming and transferring data from one system to another. In contrast, data integration combines data from different sources to deliver a unified view to users. In the case of data integration, these sources are not always from other systems but are typically from varied sources that store data differently.

How do data migration and data integration work together?

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

Data migration may be used as the foundation for successful subsequent data integration initiatives, as data migration is key 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 data integration also work hand in hand in contexts such as cloud data migration. The correct cloud integration tools can assist users in expediting cloud data migration initiatives, as the integration process gives data professionals greater visibility, organization and overall understanding when it comes to the data they need to migrate.

Combining data integration and data migration yields benefits such as the conversion of business information into actionable insights. The combination also optimizes business processes as a result of increased information exchange between multiple systems. Finally, using data integration and data migration together can increase productivity across an organization, since all data resources are more readily available and the flow of information between various systems is enhanced.

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Learn the latest news and best practices about data science, big data analytics, artificial intelligence, data security, and more. Delivered Mondays and Thursdays