Understanding Data Migration: Best Practices and Strategy
Big data drives most modern businesses, and big data never sleeps. That means data integration and data migration must be well-established, seamless processes, regardless of whether the data is migrating from inputs to a data lake, from one repository to another, from a data warehouse to a data mart, or in or through the cloud. Businesses that do not have a qualified data migration plan may go over budget, end up with overwhelming data processes, or discover that their data operations are performing below expectations.
What is data migration?
Data migration is the process of moving data from one system to another. While this may appear to be a simple change, it involves a change in storage and a database or application.
Any data migration will involve at least the transform and load steps in the context of the extract/transform/load (ETL) process. This means that extracted data must go through a series of functions before being loaded into a target location.
Organizations migrate data for a variety of reasons. They may need to redesign an entire system, upgrade databases, create a new data warehouse, or merge new data from an acquisition or another source. Data migration is also required when deploying a new system alongside existing applications.Why is a Data Migration Strategy Necessary?
Regardless of the specific reason for the data migration, the overall goal is to improve performance and competitiveness.
But you must get it right.
Inadequate migrations can result in inaccurate data with redundancies and unknowns. This can occur even when the source data is perfectly usable and adequate. Furthermore, any issues in the source data may be exacerbated when it is introduced into a new, more sophisticated system.
A comprehensive data migration strategy avoids a bad experience that causes more problems than it solves. In addition to missing deadlines and exceeding budgets, preliminary plans can lead to migration projects failing. Teams must give migrations their full attention when planning and strategizing the work rather than making them subordinate to another project with a large scope.
A strategic data migration plan should take into account the following critical factors:
- Understanding the data — Before migrating, source data must be thoroughly audited. If this step is skipped, unexpected problems may arise.
- Cleanup — Once you’ve identified any problems with your source data, you must fix them. Because of the project’s scope, additional software tools and third-party resources may be required.
- Maintenance and protection: Data degrades over time, rendering it untrustworthy. This means that controls must be in place to ensure data quality.
- Governance entails tracking and reporting on data quality to gain a better understanding of data integrity. The processes and tools used to generate this information should be highly usable, with functions automated where possible.
A data migration plan should include a process for bringing on the right software and tools for the project and a structured, step-by-step procedure.
Data Migration Strategies
There are several approaches to developing a data migration strategy. An organization’s specific business needs and requirements will help determine what is most appropriate. Most strategies, however, fall into one of two categories: “big bang” or “trickle.”
“Big Bang” Migration
A big bang data migration completes the entire transfer in a short period. While data is processed by ETL and transferred to the new database, live systems experience downtime.
The appeal of this method is that everything happens in a single time-boxed event that takes relatively little time to complete. However, the pressure can be intense because the company operates with one of its resources offline. This puts the implementation at risk.
If the big bang approach makes the most sense for your company, consider going through the migration process ahead.
In contrast, trickle migrations complete the migration process in stages. During implementation, the old and new systems run parallel, eliminating downtime or operational interruptions. Real-time processes can keep data migrating indefinitely.
Compared to the big bang approach, these implementations can be quite complex in design. However, the added complexity usually reduces rather than increases risks if done correctly.
Best Data Migration Practices
There are some best practices to keep in mind regardless of which implementation method you use:
- Back up the data before executing. You cannot afford to lose data if something goes wrong during the implementation. Before proceeding, ensure that backup resources are available and have been tested.
- Maintain your strategy. Too many data managers create a plan and then abandon it when things go “too” smoothly or get out of hand. The migration process can sometimes be complicated and even frustrating, so plan for it and stick to it.
- Test, test, and test again. Test the data migration during the planning and design phases and during implementation and maintenance to ensure that you will eventually achieve the desired result.
6 Crucial Steps in Data Migration Planning
The specifics of each strategy will vary depending on the organization’s needs and goals, but in general, a data migration plan should follow a common, recognizable pattern:
1. Explore and Evaluate the Source
Before migrating data, you must first know (and understand) what you’re migrating and how it fits into the target system. Learn how much data is being pulled over and what that data looks like.
There may be data with many fields that do not need to be mapped to the target system. There may be missing data fields within a source that will require a pull from another location to fill the gap. Consider what needs to be migrated, what can be left behind, and what might be missing.
Run an audit on the data contained within after meeting the requirements for data fields to be transferred. If there are poorly populated fields, many incomplete data pieces, inaccuracies, or other issues, you may want to reconsider whether you need to migrate that data in the first place.
If an organization skips this step and assumes an understanding of the data, it may waste time and money on migration. Worse, the organization may discover a critical flaw in the data mapping that halts all progress.
2. Define and Design the Migration
Organizations decide whether to do a big bang or a trickle migration in the design phase. This includes sketching the solution’s technical architecture and detailing the migration processes.
You can define timelines and any project concerns after considering the design, the data to be pulled over, and the target system. The entire project should be documented by the end of this step.
It is critical to consider data security plans during planning. Any data that must be safeguarded should be safeguarded throughout the plan.
3. Build the Migration Solution
Taking a “just enough” development approach to migration is tempting. However, because you will only implement it once, you must get it right first. A common strategy is to divide the data into subsets and build each category one at a time, followed by a test. If a company is working on a big migration, it might be best to build and test at the same time.
4. Carry out a live test
The testing process does not end after the code has been tested during the build phase. To ensure the implementation’s accuracy and the application’s completeness, it is critical to test the data migration design with real data.
5. Flipping the Switch
Following final testing, implementation can begin as specified in the plan.
Set up a system to audit the data once the implementation is complete to ensure the accuracy of the migration.
Migrating Data to the Cloud
Organizations are increasingly migrating some or all of their data to the cloud to improve market speed and scalability and reduce the need for technical resources.
Previously, data architects were tasked with deploying large on-premises server farms to keep data within the organization’s physical resources. One of the reasons for pushing ahead with on-site servers was a concern about cloud security; however, as powerful platforms adopt security practices that bring them up to par with traditional IT security (and, of course, in compliance with the GDPR), this barrier to migration is being overcome.
With the right cloud integration tools, customers can speed up cloud data migration projects (iPaaS) using a scalable and secure cloud integration platform. Drag-and-drop functionality in Talend’s open-source, cloud-native data integration tools makes complex mapping easier. Our solution is efficient and cost-effective because it is built on open-source software.
How to Begin with Data Migration
Data migration is on the horizon if your organization is upgrading systems, moving to the cloud, or consolidating data. It’s a big and important project, and the data’s integrity requires that it be done correctly.