Phase one
A Migration Readiness Assessment (MRA) is a structured process that helps to identify, evaluate, and prepare for the transition from a legacy data warehouse to a new platform, such as Snowflake. It covers various areas including technical readiness, operational readiness, and business readiness.
1. Technical Readiness
This involves assessing the current technical environment, identifying the software, hardware, and infrastructure used, and evaluating the compatibility with Snowflake. It also covers the assessment of data volume, data type, and the complexity of existing ETL pipelines.
2. Operational Readiness
This aspect reviews the operational processes including data governance, data management, data security, and privacy requirements. It also evaluates the processes around data ingestion, data refresh, data quality, and report generation. You should also assess how well the team is ready to handle operations in a new environment and understand if there's a need for new skill development.
3. Business Readiness
This relates to understanding the business requirements and expectations from the migration, including expected benefits, ROI, and timelines. The readiness of end-users to adapt to a new platform or interface also falls under this category.
Output
The goal of an MRA is to identify any gaps, challenges, or potential risks that might hinder the migration process and to come up with strategies to mitigate them. The assessment can help create a detailed migration plan which includes the choice of the right migration strategy (like lift-and-shift, application rewriting, etc.), migration order of the data warehouse components (like schemas, tables, ETL jobs, etc.), required resources, timelines, etc.
Phase two
Phase three
During the go-live phase of a project, parallel operations and automated data reconciliation are performed to ensure a smooth transition and to identify any discrepancies between the old and new environments. This iterative process continues until the team has achieved optimal performance, minimized risk, and improved data quality for a successful project launch.
How to start?