You're facing a complex system overhaul. How do you ensure smooth data migration in a legacy setup?

Define the scope of data needs to migrated, make sure to move the only needed chunks rather importing all. While do so also assess necessary strategies to avoid potential bottle necks. If possible dry run once in a test environment before doing it on a actual setup. While doing carefully observe the progress to mitigate the potential breakthrough in actual run

Every migration project starts with assessment. Assuming migration/upgrade is a must (either due to feature availability, HW/SW EOL etc.) the first assessment is what is needed (business wise) and which parts of the services needs redesign. Knowing in advance the platform sourcing channels/data, and what is the expected outcome, plus understanding the current legacy limitations, will allows us to better design the expected migrated platform, so migration will be smooth, data corruption will be minimized and overall project migration timeline will be within agreed SLA. Building a robust testing plan to check each migrated interface will ensure this process will go live smoothly.

Plan to fail seriously devise strategies where the potential loss of data might happen, where the data get corrupted, where the data is not properly aligned with schema, where the data might lose its actual meaning. Have the blueprint ready, migrate into steps no big bang approach. Ensure each stage of data is migrated successfully cross check, check the data manually or use any automated AI to validate. On completing two stage check the entire received data also do the same for each proceedings.

Detailed planning should include getting all data source samples in advance, building a test environment to test the inputs and outputs of every sources channel/data and to see that it matches the business needs, decide what data to keep (migrate) and what to leave behind, both in terms of data type and data retention (for example; it might be better for the business to perform initial load of the last 3 months of the data from its own DWH to the new platform instead of migrating the old data which might contain corrupted or non-relevant data from past years), agree with the customer on the validation process and ensure exit criteria are planned and met.

Listing here some examples which work well for both legacy and new platforms. - Tools for API testing (SoapAPI, Postman etc.) - Tools for Kafka validation (TopologyTestDriver, Grafana etc.) - Tools for file/text comparison (BeyondCompare etc.) Naturally, the set of tools depends on the availability (licensing) and specific needs of the QA team.

Once a testing plan has been communicated and approved, a daily report of the testing passed/failed tests (with reason of failure) should be distributed and shared with the relevant stakeholders at the end of each day, to clearly state the progress (percentage of tests performed out of the total, amount of failures and their criticality, for each issue detected, the case opened for further analysis and solution by R&D etc.).