There are many similarities between data migration and data integration, but there are also some important differences. Learn what they are.
While data migration and data integration are considered interchangeable, the two data strategies play very different roles in the data management and preparation lifecycle. Understanding the difference between the two and the unique value they each add to big data is critical. In this guide you will learn more about the difference between data migration and data integration, so you can 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 can arise, among other things, from the need to modernize databases, build new data warehouses and/or merge new data from sources.
Key Data Migration Features
Some of the key features of data migration tools are:
- Functionality: The functionality of a tool should cover scheduling, scheduling tasks, organizing workflows, data mapping and profiling, ETL tools, and post-migration audits.
- Handling data sources and target systems: A data migration tool must 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 more flexibility and scalability in this area than on-premises tools because on-premises tools are subject to hardware parameters.
- Intuitiveness and ease of use: Intuitive and user-friendly solutions save users time. Users should consider solutions that are not only intuitive and easy to use, but also backed up with exceptional technical support.
Key Data Migration Tools
Popular data migration tools include:
What is data integration?
Data integration refers to the process of merging data from heterogeneous sources into one data warehouse or database. Data integration is usually implemented to support decision making and better data quality and data analysis.
TO SEE: Top tools for cloud and application migration (TechRepublic)
Large enterprises often use data integration to create data warehouses, which provide users with more powerful reporting, querying, and analytics capabilities. They also present data in a unified format.
Key Data Integration Features
The standard feature set of data integration tools includes:
- Writing data to target systems: This function copies data from the source and stores the transformed version in target applications, services, and systems.
- Access data from a mix of sources: Data integration enables enterprises to collect data from various 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 used by a target system.
- Designing data flows: Data integration tools can create data pipelines using various targets, transformations, and resources.
Key data integration tools
Examples of data integration tools include:
What is the difference between data migration and data integration?
Frequency
When implementing a new application, data migration takes place once. As a one-time activity, the initial load contains huge volumes of data. This makes it expensive to resolve post-migration data issues. Therefore, it is critical to ensure that the migration is fully prepared in advance and handled correctly.
On the other hand, data integration is a continuous process that supports day-to-day business operations. It’s about managing incremental changes to data. Being a continuous process, data integration is easier to adopt and change over time compared to data migration.
Usage
Data integration enables teams to consolidate applications within an organization or combine applications from multiple organizations. It simplifies reporting, analysis and business intelligence and contributes to new organizational efficiencies.
Data migration is essential when organizations want to upgrade or completely replace their current systems. It is often run when there is a need to expand system and storage capacity, move IT services to the cloud, or introduce a centralized database to break down data silos.
Goal
Data migration involves selecting, preparing, extracting, transforming and transferring data from one system to another. Data integration, on the other hand, combines data from different sources to give users a unified view. In the case of data integration, these sources do not always come from other systems, but usually from different sources that store data differently.
How do data migration and data integration work together?
Data migration and data integration have different but essential functions in the management and usability of today’s business applications. They both revolve around the transfer of data, but they transfer data for completely different purposes.
Data migration can 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 go hand in hand in contexts such as: cloud data migration. The right cloud integration tools can help users accelerate cloud data migration initiatives as the integration process gives data professionals greater visibility, organization, and overall insight into the data they need to migrate.
Combining data integration and data migration delivers benefits, such as turning 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 within an organization as all data sources are more readily available and the flow of information between different systems is improved.
