You're navigating a complex data architecture. How do you maintain data integrity during migration processes?

Evaluate the data to be migrated, including its structure, quality, and dependencies. Identify any potential issues that need to be addressed. Decide whether to use a "big bang" approach (migrating all data at once) or a "phased" approach (migrating in stages). Each has its advantages and risks. Develop a comprehensive plan covering data mapping, transformation rules, and validation processes. Create a data mapping document that specifies how data from the source system will be mapped to the target system. If data transformation is needed (e.g., formatting changes), clearly define the rules and ensure they are documented and tested.

Begin by conducting a thorough assessment of the existing data architecture and mapping out the migration plan. Ensure all data is backed up before starting the migration. Implement robust data validation checks at each stage of the migration process to verify data accuracy and consistency. Use automated tools and scripts to handle data transfer efficiently and reduce the risk of human error. Establish clear protocols for handling discrepancies and errors. Monitor the migration process closely with logging and real-time tracking to identify and address issues promptly.

Before migrating data map the metadata. Profile the data and understand the underlying data architecture. 1. Carefully plan the mapping between the as state vs the future state. 2. Validate with the business owners the data map 3. Divide the data migration into smaller sprints. 4. Deliver the first sprint as a pilot and validate the result. 5. Once validated with the process and the result in pilot, accelerate on the other sprint delivery.

Maintaining data integrity during complex migration processes requires meticulous planning and careful execution. Begin by conducting a thorough assessment of the existing data architecture to understand its structure and identify potential risks. Develop a detailed migration plan that includes a comprehensive mapping of data sources, transformation rules, and target systems. Implement robust validation and verification procedures to ensure data accuracy at each stage of the migration, such as data cleansing and pre-migration testing. Establish checkpoints and roll-back strategies to address any issues promptly and minimize disruption.

Documenting is in many cases the great forgotten. During implementation we tend to prioritize development, delivery, testing or bug fixing tasks, leaving the documentation for overtime that we never have. In my experience, the lack of clear and complete platform documentation ends up being a problem. Keeping functional and operational documentation updated must be essential. In this way we will facilitate the knowledge sharing for all team members and the onboarding of new ones. To do this, we can use tools that provide collaborative workspaces such as the Atlassian's suite of products.

Evaluate what things make complex the data migration: different data storage, storage capacities like data format, infrastructure, function or procedures, how transactional systems use it to remodel schema or tables, etc. Based in these points, the data integrity can address in efficient and effective way.