AWS- Oracle Database to Amazon Redshift

Our Approach to Migration

• Copy the initial data from the on-premises data warehouse production environment to an Amazon Redshift development environment for testing.

• Migrate and create ETL jobs throughout migration, and use the development environment to test the ETL scenarios.

• Take the latest copy of on-premises data warehouse production data and migrate it over to Amazon Redshift. Do this until the Data Freeze Date, which is the date from when the Oracle systems is used by both ETL systems to update the source database and Amazon Redshift in parallel.

• Use the new ETL jobs to update Amazon Redshift from the source system while the old ETLs are updating.

• Perform business validation on Amazon Redshift and identify a cutover date to migrate all the users.

Preceding steps into a migration plan that consisted of five major milestones: 

1. Data warehouse discovery and data evaluation.

Our data architects analyzed the Oracle data warehouse for input data sources, data ingestion pipelines, and data transformation requirements. They also analyzed it for the final data model required by the customer’s business processes and reports. remodeling data warehouse and ETL processes. That effort identified challenges with three data warehouse components:

Input data sources

Data ingestion pipelines

Data consumption

2. Data warehouse remodeling.

3. Data and ETL migration.

4. Reports and business process migration.

5. Go live.

Another major difference between the data warehouses is the data storage architecture. The Oracle database is architected as a row-based data store. Amazon Redshift is a columnar data store, which supports its massive compression capabilities.