Database migration is all about transferring data between different database management systems (DBMS). It ensures that your data remains intact, consistent, and secure during the move.
Homogeneous database migration involves moving data within the same DBMS. It's a straightforward process since the source and target systems are identical. For example, migrating from SQL Server on-premise to SQL Server on Azure.
For guidance, you can refer to the Cloud to Warehouse Native Migration guide. Additionally, you might find it useful to check out Data Best Practices to ensure a smooth transition.
Heterogeneous database migration involves moving data across different DBMS. This type is more complex due to differing data structures and query languages. An example is migrating from Microsoft SQL Server to PostgreSQL.
For more information on such complex migrations, you can look into Moving from POC to Production. This guide provides insights into handling different environments and setups. Also, the Setup Checklist can be a handy reference to ensure you cover all necessary steps.
ETL stands for Extract, Transform, Load. Extract data from the source system. Transform it to meet the target schema. Load it into the destination database.
Learn more about best practices for ETL.
Check out SQL best practices.
Understand how to utilize incremental reloads.
Schema mapping involves aligning data structures. Ensure tables and columns in the source match the target. It simplifies data transformation and loading.
Explore data mapping.
Follow guidelines for creating a metric source.
Check the data pipeline overview.
Data integrity ensures data remains accurate and consistent. It's crucial during migration to avoid corruption. Validating data at each step helps maintain integrity.
Review data integrity best practices.
Understand exposure duplication.
Learn about exporting data from Statsig.