t cht lk a test deployment of your data on your new platform to validate that it operates as expected and is free of bugs . The test doesn ’ t have to produce a complete production environment – your goal is to set up a simple test environment where you can push some real-world data into your new platform and assess its behavior .
t cht lk a test deployment of your data on your new platform to validate that it operates as expected and is free of bugs . The test doesn ’ t have to produce a complete production environment – your goal is to set up a simple test environment where you can push some real-world data into your new platform and assess its behavior .
Containerized deployment of your data platform is helpful here because containers make it easy to deploy a new platform quickly and connect it to your data .
That approach is simpler in many cases than standing up a complete production platform .
Performance comparison . With a test environment up and running , you can run performance tests on both your old and new platforms . This is another step in the validation process and helps ensure that your new platform meets requirements . need to implement on the new platform to meet them . Be sure to consider how requirements may vary as data volume grows and during periods of varying load on your system .
Data conversion . Data conversion – which means reformatting data to fit the new platform – may be a simple or complicated process , depending on the degree of difference between the old and new platforms . But in most cases , at least some level of conversion is necessary .
You don ’ t need to take your old data platform offline while performing conversion . To minimize disruption , keep it running but make a copy and convert data based on that . You ’ ll need to perform some additional conversion later to sync your data just before taking the new platform live , but this approach lets you minimize conversion-related downtime .
Test deployment . With your assessment complete and ( most of ) your data converted , you ’ re ready to run to the new platform .
Switching over . Assuming your test environment passes all validations , you are ready to switch your live operations from the old to the new platform . Doing so will require you to take the old platform offline , perform any final data conversions necessary to sync data between the two platforms and , finally , redirect requests from your applications
You should expect some amount of downtime during this process ; a few hours is typical . But by carefully planning , preparing and testing the data migration ahead of time , you minimize your risk of unexpected problems and help ensure that you don ’ t end up with days or weeks of downtime , leading to critical business disruption .
Conclusion : Data migration without disruption
You shouldn ’ t have to accept downtime for your business to take advantage of new data platforms .
With careful planning and testing prior to data migration , as well as data conversion strategies that minimize the time your data platform is offline , you can achieve benefits like improved ROI or infrastructure modernization without paying the price in the form of data platform downtime . p
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