Finding reasons to migrate data to a new platform is easy. Data migration can help save money, increase flexibility and enhance reliability. What’s harder is actually performing data migration.
If you’re dealing with data that powers continuous, mission-critical business operations, getting the data from an old platform into a new one with minimal disruption to your organization is no simple feat. Here are five ways to make your data migration process as seamless as possible.
5 steps to migrate data
- Assess the data.
- Convert the data.
- Deploy the test.
- Compare performance.
- Switch over.
Why Do Businesses Migrate Their Data?
Before diving into best practices for performing data migration, let’s talk about common goals for business in migrating data. One of the most common objectives is saving money. Businesses often rely on licensed products to host their data, but after evaluating open-source options, they decide that a free and open-source alternative will allow them to meet their data needs at a lower cost.
Indeed, open-source databases like PostgreSQL and MySQL are great options these days if you want to save money without compromising performance. You might need to invest a little more effort in management if you opt for an open-source solution, but it’s worth it for businesses looking to streamline data platform costs.
Gaining more flexibility is also a driver for data migration. If you’re tied to a specific data platform, you may be wed to a particular vendor’s infrastructure or product ecosystem. This can make it hard to adopt a multicloud strategy, for example, because the data platform you use may integrate poorly with clouds other than those that the platform vendor chooses to support. But if you migrate data to an open-source, vendor-agnostic platform, it becomes easier to deploy workloads wherever you need.
A general goal of modernizing architectures and environments can motivate businesses to migrate their data as well. For instance, you might want to redeploy your applications using containers, but because you store your data in a legacy platform that was not designed for cloud-native environments, you choose to migrate it so it integrates more seamlessly with your modernized application stack.
How To Migrate Data
Figuring out how to migrate data is where things get tricky — when you perform a migration, any workloads or processes that depend on your data will break while the migration is in progress. For a retailer, sales may stop while data migration is occurring because sales apps can’t operate without the databases they depend on. Financial institutions might need to take banking apps offline. Manufacturers may face disruptions to factory operations that depend on data platforms. And so on.
So, how do you migrate your data without disrupting your business and depriving yourself of revenue? Approach data migration in a coherent, systematic way by following these five steps.
Assess the Data
Start by assessing the requirements that your data platform needs to support, then determining which configuration you’ll 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.
Convert the Data
The complexity of data conversion, or reformatting data to fit the new platform, depends 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.
Deploy the Test
With your assessment complete and most of your data converted, you’re ready to run a test deployment of your data on your new platform to validate that it operates as expected and is free of bugs. 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 — which host apps in portable, software-defined environments or modes of data transportation between computer systems — make it easy to deploy a new platform quickly and connect it to your data. Using containers, you can quickly validate that apps connect to data as expected without running a complete environment.
With a test environment up and running, you can run performance tests on both your old and new platforms. Collect metrics such as average throughput, memory consumption and central processing unit utilization rates of your workloads in both environments. Using these metrics, you can compare performance and confirm that the migration doesn’t lead to lower responsiveness or higher resource consumption. This is another step in the validation process and helps ensure that your new platform meets key requirements.
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 to the new platform.
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.
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 return on investment or infrastructure modernization without paying the price of data platform downtime.