Data Migration and its Tools

Data migration is the process of moving data from one location to another, one format to another, or one application to another. Generally, this is the result of introducing a new system or location for the data. The business driver is usually an application migration or consolidation in which legacy systems are replaced or augmented by new applications that will share the same dataset.



Below are the types of Migration:

  • Storage migration. The process of moving data off existing arrays into more modern ones that enable other systems to access it. Offers significantly faster performance and more cost-effective scaling while enabling expected data management features such as cloning, snapshots, and backup and disaster recovery.

  • Cloud migration. The process of moving data, application, or other business elements from either an on-premises data center to a cloud or from one cloud to another. In many cases, it also entails a storage migration.

  • Application migration. The process of moving an application program from one environment to another. May include moving the entire application from an on-premises IT center to a cloud, moving between clouds, or simply moving the application's underlying data to a new form of the application hosted by a software provider.


Best Data Migration Tools


#1) IRI NextForm

IRI NextForm is available in multiple editions as a standalone data and database migration utility, or as an included capability within the larger IRI data management and ETL platform, Voracity.


You can use NextForm to convert: file formats (like LDIF or JSON to CSV or XML); legacy data stores (like ACUCOBOL Vision to MS SQL targets); data types (like packed decimal to numeric); endian states (big to little), and, database schema (relational to star or data vault, Oracle to MongoDB, etc.).


Key features:

  • Reaches, profiles, and migrates data graphically in IRI Workbench, a familiar and free Eclipse IDE for job design, deployment, and management.

  • Supports close to 200 legacy and modern data sources and targets, with the ability for more through custom I/O procedures or API calls.

  • Uses standard drivers like ODBC, MQTT, and Kafka for data movement, and supports local, cloud and HDFS file systems.

  • Data definition and manipulation metadata are in simple, self-documenting 4GL text files that are also represented in dialogs, outlines, and diagrams for easy understanding and modification.

  • Builds job tasks or batch scripts for execution, scheduling, and monitoring from the GUI, command line, etc., plus secure team sharing in a Git Hub for version control.


#2) Xplenty

Xplenty is a cloud-based data integration platform. It is a complete toolkit for building data pipelines. It p