Summary

About

Process

• Moving data:

  This can be from on-premises servers to the cloud, between different databases, or from one application to another. 
• Data preparation:

  This includes cleaning, validating, and preparing the data for the migration process. 
• Transformation:

  Data may need to be transformed to fit the structure and format of the new system. 
• ETL process:

  A common method for data migration, involving extraction from the source, transformation, and loading into the target system. 
• Testing and validation:

  Ensuring the data is accurate and complete in the new system. 
• Decommissioning the old system:

  The final step, where the old system is shut down after successful migration. 

Source: Google Gemini Overview

Importance

• Modernization:

  Data migration enables organizations to adopt new technologies, such as cloud computing, and improve their data infrastructure. 
• Consolidation:

  It allows for consolidating data from multiple sources into a single, unified repository, improving accessibility and analysis. 
• Efficiency:

  Migrating to more efficient systems can lead to cost savings and improved performance. 
• Scalability:

  Cloud migration allows for easier scaling of resources to meet changing business needs. 
• Data quality:

  Data migration can be an opportunity to improve data quality through cleaning and transformation processes. 

Source: Google Gemini Overview

Examples

• Moving data from an older database to a newer, more powerful one. 
• Migrating data from on-premises servers to a cloud platform. 
• Consolidating data from different departments or acquisitions into a central data warehouse. 
• Upgrading an application and migrating the associated data. 

Source: Google Gemini Overview