How do you migrate data warehouses?

It's important to make sure your documentation is complete and your data is clean. This is a very good opportunity to do both of these if you haven't been keeping up with these duties.

Identify any gaps between the current data warehouse and your desired state. Look for opportunities to enhance data quality, increase performance, and address any shortcomings in the current system. Clearly define the scope of the migration project. Determine what data will be migrated, such as specific datasets or historical records. Set objectives for the migration in terms of timelines, performance benchmarks, and data integrity goals.

I was involved in a significant data system migration project. The primary objective of the project was to transition from a monolithic architecture to a microservices-based architecture, which also entailed moving data from a SQL Server-based data warehouse to Snowflake. One key aspect that stood out during this migration was the emphasis on understanding the complexity and commitment required for the transition. It was recognized early on that this migration would be a substantial undertaking, and thorough planning and documentation were essential.

one key guideline to remember is not to take on too much at the same time. the new platform, re-designing data models, introducing new data sources, and building additional analytics are common requests that can get mixed with migration projects. Keeping the focus on migration first can help make the project move smoothly. Planning architecture, approach and even tool set for historical data migration and incremental data feed from source systems can help the new data warehouse stable when it goes live.

Performance optimization and monitoring are essential to designing the target state in the data warehouse migration process. This perspective focuses on ensuring that the new data warehouse operates efficiently, delivers optimal performance, and meets the organization's performance goals. Define precise performance requirements and objectives for the target data warehouse. Identify key performance indicators (KPIs) that will be used to measure and evaluate the system's performance.

Planning architecture, approach and even tool set for historical data migration and incremental data feed from source systems can help the new data warehouse stable when it goes live.

Before the migration begins, perform comprehensive data validation on the source data to identify any data quality issues, inconsistencies, or missing data. Addressing these issues proactively ensures that the migrated data is of high quality. Verify the data mapping between the source and target data warehouse to ensure the data is correctly transformed and loaded into the new environment. Conduct data reconciliation checks to confirm that all data has been accurately migrated.

To ensure a smooth transition, the migration was carried out incrementally. Rather than attempting to migrate the entire system in one go, we opted to move different components of the system one by one. This approach allowed us to identify and address any issues or compatibility issues as they arose, minimizing the risk of system disruptions. In summary, the migration project I was involved in was characterized by a clear recognition of the challenges and opportunities presented by the transition from a monolithic system to microservices and the migration of data to Snowflake

Making it in multiple steps can help too. If there is significant data transformation involved, it would help to build a copy of data as it was in the old data warehouse and then create the data transformation can reduce data quality and integrity issues.