Data Migration Plan: 7 Steps To Follow

What is a data migration strategy?

Data migration is the process of moving data from one or more systems into a different system. Migration can take many different forms, including moving data from an on-premise location to a cloud database system. More recently, this may also involve migrating data from one cloud system to another. In its simplest form, data migration involves migrating data from a legacy system into a new or more modern system.

The purpose of migrating data differs for various organizations. The purpose of the data migration project should also act as a starting point for the data migration strategy put into place. Some reasons for data migration include upgrading data infrastructure for security purposes or to keep systems relevant. Another major reason for implementing a data migration strategy is as part of a larger digital transformation initiative. The ultimate reason behind implementing any data migration strategy is to enhance performance, productivity, and competitiveness.

Having said this, moving data from one place to another is not as simple as it sounds. If it is done without care and planning, it can affect your business negatively. A Major reason for a failed data migration initiative is from lack of a proper data migration strategy and execution plan. Database migration is truly a specialized activity that requires a robust methodology that involves effective planning to mitigate the risks that may be encountered. On top of avoiding the negative aspects of the data migration process, engineers should also look to maximize opportunities for the success and modernization of the new data infrastructure.

Therefore, the data migration strategy can be described as a plan that is used to facilitate the transfer of data from one platform to another. During the execution of the plan, participants should aim to avoid blowing past allocated budgets and missing deadlines, all of which could lead to project failure. The strategy should also aim to reduce the business impact of moving data. The data migration strategy should take certain factors such as data auditing, cleanup, maintenance and protection, and governance into consideration, as we will discuss next.

What should a data migration plan include?

When deciding on what to include in your data migration plan, you have to bear in mind that there is no one-size-fits-all strategy. Each approach may have similar steps but with a custom and unique take since every data set is unique. The reason behind the migration of data may also differ and be unique from organization to organization. The ideas discussed in this section can be used as a general background for what needs to be included in your migration plan. The goal is to have an efficient migration plan that avoids common challenges that may arise as a result of not having a well-planned migration strategy.

As stated in the preceding section, a good data migration plan will put some factors into consideration, some of which may be things to do and others to avoid. The plan itself will contain the following critical factors and tasks:

Data audit: This simply means carrying out a complete data audit of the source data to know what data is present before embarking on migrating the data. This may sound irrelevant, but, ignoring this simple process may result in unexpected issues that may hamper your data migration plan.
Cleanup: This is carried out when an anomaly is noticed in your source data after auditing. This step is used to iron out any issue that may occur when trying to migrate the data due to incorrect formatting or data quality issues.
Maintenance and protection: Set up control measures in place to ensure data quality. This step helps to ensure reliability after the migration is completed.
Governance: This ensures that data integrity is understood as the data would be tracked and reported on to enable data quality.

There are two major approaches to migrating data that can be used: the “Big Bang” and the trickle migration. With the “Big Bang” approach, the full transfer of data is completed from one source system to another in a single operation within a brief period. The trickle migration approach is where the migration process happens in phases, broken down into small sub-migrations with each having its own scope, goals, and deadlines.

The Big Bang migration is most often than not performed during the weekend or a scheduled downtime period. Its simplicity is beneficial since the whole process occurs in a single time-boxed event. The downside is that the live system will experience downtime during the migration process. During the migration, every resource is channeled toward the migration which can be a headache for organizations that require constant uptime for their systems. The risk for the data within this approach is that it is an “all-or-nothing” approach. It is extremely hard to test each part of the migration when taking this approach so the risk of data corruption or the need to rollback is relatively high. However, in some cases, this is the only approach that will make sense.

The trickle migration, in contrast, runs the old and the new system in parallel as data is being moved in phases. This eliminates downtime or operational disruptions and makes it easier for the data migration team to ascertain the success of each phase. It also allows the team to re-work any failed processes if they occur. As the team faces challenges or failures, they can learn and utilize the lessons learned along the way in subsequent phases.

Compared to the “Big Bang” method, the trickle migration process takes a longer time to achieve. The complex design of having two systems run simultaneously while consuming many resources and large amounts of effort can make trickle migrations more difficult to execute.

Additionally, a structured data migration plan should have step-by-step procedures that are adhered to by all parties involved in the migration process. You also have to decide the most appropriate migration approach based on your business needs and technical constraints. We will now look at how to create a comprehensive data migration plan and showcase seven recommended steps to follow. We will also explore what should be included in your strategy to guide you into forming a good and sufficient data migration plan.

Step 1: Identify the data format, location, and sensitivity

This first step in the data migration plan has to do with you accessing your data. In this step, you will explore:

What data should be migrated?
Where is the data stored?
What is the current data format?
Is it compatible with the new location or will it need reformatting?
Where should the data be sent to?

In doing this, you may discover data with loads of fields that may not be needed in the target system. You may also discover missing data fields within the source data that may need to be filled from another source and other areas of improvement.

By carrying out this process during the pre-planning stage, the information uncovered can mean potential risks and obstacles are spotted quickly. Discovering these issues can help you avoid ugly situations that would have arisen when moving the data. It is beneficial to invest time into planning and transforming the data at this stage to overcome any challenges that may be faced when migrating.

Step 2: Planning for the size and scope of the project

This stage involves the allocation of the resources that would be needed to migrate your data to the new target system. This will be largely determined by the size and scope of the project. Knowing the size and scope, an effective budget and a flexible timeline for the migration can be drawn up.

The key aim of planning this step is to be able to filter out any unwanted, unnecessary, or legacy data not needed for the migration. By sticking to a defined scope, the project can be executed more smoothly with a lower risk of runaway costs and scope creep. When deciding on what data to migrate, consider if it would interfere with your business operations, contribute to downtime, or have any other direct impacts on users and customers. This is done by performing a high-level analysis in consultation with data users and key stakeholders explaining the need for the project and the expected scope. The aim of this is to minimize the impact the migration may cause on them.

Step 3: Make sure all your data is backed up

It is very important to ensure that all your data is backed up before commencing any migration of data. At a minimum, the data and files that you would want to migrate should be backed up. This would make sure that if any unforeseen circumstance occurs when migrating the data leading to corrupt, incomplete, or missing data or files, you can restore them to the original state using the backup file.

It is also recommended that you store your data in multiple forms of backups like the cloud service or a local backup system. This is so that even if the online servers are not active, the off-site backup can be used to restore the database quickly when an issue, such as a power outage, arises.

Step 4: Assess staff and migration tools

Assessing your capability to know if you can perform the data migration is the next step in your data migration plan. It is important to do an honest assessment of your staff's capability to handle the tasks ahead. If required, having an expert or an experienced professional on board will help the entire process go smoothly and give confidence in the ability to handle any issues that arise.

If the scope of data migration is big, it is also good to invest in first-class data migration tools. It may also be better to outsource the entire process to an experienced team if your staff does not how the required skillset to handle the migration. The right tools can also reduce the need for expertise since many tools aim to make the process less complex and more fault-tolerant.

Step 5: Execute the data migration plan

Once you have decided on the conditions stated above, it is now time to execute your data migration plan. The data migration plan you’ve created will guide you on how to go about each step. The plan will dictate what permissions to apply, how to extract the data from the source and migrate it to the target system, ensure the cleaning and transformation of the data to suit the target system and how to transfer it onward into the new location.

At every point of the migration process, close monitoring by the team can help identify and detect any lapses that may occur and correct them where necessary. All of these steps are required to execute a successful data migration.

Step 6: Test of the final system

After the whole process of migration is completed, it is now time to test the final system to evaluate the results within the migrated data. The first thing to do is to verify the integrity of the data transfer to the target system. This is done by checking if there are any corrupt, missing, or incorrect data or files. It is recommended that this is done through an automated test to identify any issues.

Conduit unit, system volume, web-based application, and batch application tests are all different tests that can be carried out to ensure that the data was migrated correctly, securely, and to the right locations. Having said that, if the entire process was been monitored at each stage, few or no surprises may arise after the check as verification likely happened as the process was happening.

Step 7: Follow-up and maintenance of the data migration plan

Finally, an elaborate audit of the whole migration process needs to be done after the migration process is completed. During this step, you will make sure that every issue has been resolved and that there are no obstacles that may cause problems in the future. This step also involves having a retrospective meeting of the data migration process with the various teams and stakeholders that were involved. Looking back on the lessons learned or challenges encountered can help your team to have a more effective process in the future. After a successful post-migration audit, the old system can then be retired and decommissioned.

Bonus: Using Arcion as part of your data migration plan

As mentioned, having the right tools at your team's disposal can do miracles for making data migration easy and effective. One such data migration tool is Arcion. Instead of forcing engineers to work through configuration files and code, Arcion allows users to create a no-code data pipeline for data migrations. In minutes, a pipeline can be used to effectively migrate data from multiple sources into a new destination (or even multiple destinations). Arcion also offers the ability to accommodate both “big bang” and trickle data migration approaches by using CDC to transfer and replicate data.

On top of these advantages, Arcion offers both self-managed on-premise and fully-managed cloud products to fit your exact needs.

By using Arcion, it’s easy for organizations to build pipelines for data migration using many different data sources. With no code, easily move data from a source database to a new database or big data platforms like Snowflake or Databricks. For many use cases, Arcion is much easier and more flexible than the built-in migration tools supported by the major database providers.

Benefits of using Arcion include:

Native support for the most popular databases and data warehouses
Agentless CDC ensures there is zero impact on the source data systems. Arcion reads directly from database logs, never actually reading from the production database. No need to install a software process on the production system.
Multiple deployment types supported across cloud and on-premise installations
Configuration can easily be done through UI, with minimal effort and zero code
Automatic schema conversion & schema evolution support out-of-the-box (including SQL and NoSQL conversion)
Patent-pending distributed & highly scalable architecture: Arcion is the only end-to-end multi-threaded CDC solution on the market that auto-scales vertically & horizontally. Any process that Arcion runs on Source & Target is parallelized using patent-pending techniques to achieve maximum throughput.
Built-in high availability (HA): Arcion is designed with high availability built-in. It makes the pipeline robust without disruption and data is always available in the target database, in real-time.
Auto-recovery (patent-pending): Internally, Arcion does a lot of check-pointing. Therefore, any time the process gets killed for any reason (e.g., database, disk, network, server crashes), it resumes from the point where it was left off, instead of restarting from scratch. The entire process is highly optimized with a novel design that makes the recovery extremely fast.

Moreover, the largest wireless provider in the US used Arcion to migrate 40 Oracle databases, 12K tables, 25M partitions with 120 TB of data, with zero downtime. Check out how enterprises leverage Arcion for their database migration projects.

Conclusion

In this article, we discovered what a data migration plan is all about and what a successful migration looks like. If your organization wants to upgrade systems by moving data to the cloud or other systems, it has to come up with a robust and solid data migration plan. Data migration initiatives are big and important projects that need to be executed with care to ensure data integrity and business continuity.

As a closing statement, we also took a brief look at using Arcion as part of the data migration strategy. If you do not want to handle the database migration yourself and need a tool to make it as easy as possible, Arcion is a great platform where data can be migrated with ease. Arcion’s knowledgeable and experienced team uses modern and cutting-edge technologies to help companies migrate data reliably and with ease. To explore Arcion in more detail, schedule a personalized demo today.

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