How can you migrate data to a target system's data model?

First thing what we see before migration are kinds of data sources that needs to be migrated from ABC technology to xyz technology and then come the underlying data structure of source and destination if the tech stack is going to be same then comes data modelling where we understand how different objects are related to one another and finding out all the constraints related to particular objects with what kind of load every source will have (full load,incremental load) and then we start with the utility of migrating data to a new tech stack destination with what type of storage we are using at backend.Last but not the least comes data governance best practices including authorization access and columns and row level granular locks.

It's crucial to outline the scope and objectives. Identify what data needs to be migrated, understand the reasons behind migration, and establish clear goals. This step lays the foundation for the entire migration process.

Determine the scope of the migration project, including: Which data sources will be migrated? What types of data (e.g., tables, files) need to be migrated? What volume of data needs to be migrated? Are there any specific data subsets or historical data to be included? What are the dependencies and interrelationships between data entities? Define clear objectives for the data migration project: Ensuring data accuracy, completeness, and integrity in the target system. Minimizing downtime and disruption to business operations during the migration process. Optimizing performance and scalability of the target system. Enhancing data quality, consistency, and accessibility. Achieving compliance with regulatory requirements or industry standards.

Thoroughly analyze the structure, format, and relationships within both the source and target data models. Identify any discrepancies or mismatches between the two to anticipate potential challenges during migration. This analysis informs the strategy for mapping and transforming data.

Understand Source Data Model: Analyze the structure, relationships, and constraints of the source data model. Identify the entities, attributes, and relationships represented in the source data. Document the data types, formats, and constraints applied to each attribute. Determine any data quality issues or inconsistencies in the source data. Understand Target Data Model: Review the structure, relationships, and constraints of the target data model. Understand how data is organized and represented in the target system. Identify the entities, attributes, and relationships defined in the target data model. Document any transformations or mappings required to align source data with the target model.

Based on the analysis, design a comprehensive migration strategy and architecture. Determine how data will flow from the source to the target system while ensuring data integrity, consistency, and security. Develop a plan for handling any data transformations or conversions required.

You need to keep in mind migrate data to different RDBMS may have different limitation per data type. Data type incompatibilities may probably occur. This will lead to the need to change data types in target or source destination.

Develop scripts and programs tailored to extract, transform, and load (ETL) data from the source system to the target system. Test these scripts rigorously to validate their functionality and accuracy. Address any issues or errors encountered during testing to ensure smooth execution during migration.

Be sure that the data you’re testing with is a fair representation of the data you’re migrating. Sample data is not nearly as valuable as a subset of production data

Execute the migration process according to the defined strategy. Monitor the migration in real-time to identify any issues or bottlenecks promptly. Implement measures to optimize performance and ensure data transfer occurs within the specified timeframe and with minimal disruption to operations.

It is super important to understand the traceability and lineage of the data to ensure quality and integrity. There are several types of tests that can be performed to validate and verify data migration: Integrity testing, Consistency testing, Reconciliation testing, Performance testing, Scalability testing, Security testing. By performing a combination of these tests, you can ensure that data migration is successful and that the migrated data is accurate, complete, and secure.

To migrate data to a target system data model, I first identified the key structures and relationships in the target data model. Next, I performed an analysis of the source data to understand its format and content. Using ETL (Extract, Transform, Load) tools, developed scripts to extract the data, transform it as per the requirements of the target model and load it efficiently. Implemented mapping and transformation strategies to address discrepancies between data models. Performed testing to ensure the integrity and accuracy of the migrated data, ensuring a smooth transition to the new system. :D Some tools: Azure Data Factory, AWS Database Migration Service, Google Cloud Data Transfer Service, Apache Sqoop, Talend Data Integration.