What is data migration?
Data migration is when you move data from one computing environment or storage system to another. Organizations collect and store data for analytics. They have to move data between systems for integrated visualization, due to technology changes or because they want to move operations to the cloud. The goal of data migration is to move data efficiently and quickly to avoid or minimize disrupting business operations. It includes planning for considerations like network resources, data security, and time and transfer methods. Data migration may also involve storage architecture considerations for factors like missing data values or changing data types.
Why is data migration important?
Organizational data resides in many places—in physical storage, in on-premises servers or virtual servers, in single machines, and even in different applications. Data is also stored in many different formats and types.
Organizations move data from one location, device, or application to another for many different reasons. For example, data migration might be used for these purposes:
- Consolidate resources
- Integrate data for analysis
- Reduce storage costs
- Centralize business data
- Use new applications
- Archive legacy data
- Use data for a different purpose
- Transfer data ownership
- Improve compliance with data handling regulations
What are some data migration strategies?
There are different types of IT migration. Terms like storage migration, database migration, schema migration, application migration, and business process migration all involve data moving from one place to another. Next, we give some strategies that you can use for data migration.
Lift and shift
Lift and shift is the easiest way to migrate data. You keep the data in the same format, without any transformation, and simply transport it and store it in another location. While it's an effective strategy, it can be less useful for cloud migration. Storing the data in the same format often won’t help effectively capitalize on the benefits of cloud storage.
Use preexisting tools
There are many data migration software tools available to help organizations complete a successful migration. These vendor and open source data migration tools make the entire process much simpler from a management perspective.
For example, AWS DataSync is an Amazon Web Services (AWS) offering. It helps organizations transfer their on-premises shared file systems, object storage, or Hadoop clusters to AWS cloud storage solutions.
Move all at once or in phases
Depending on the data itself, you can choose to move everything all at once or shift the data in stages. For example, you can split up a large amount of data and perform chunked data migrations overnight over several weeks. While it’s easiest and fastest to migrate data all at once, sometimes it’s simply not possible.
Enlist specialist help
For complex migrations where there's no one on the team with prior experience, it can be wise to enlist the help of outside experts. In cloud migration to AWS, you can choose to connect with one of our AWS Partners.
What are the factors to consider before data migration?
Data migration requires planning every detail of the process. Here are some factors to consider .
Online or offline data migration
It can be time-consuming and resource-intensive to migrate a very large amount of data, even with modern networking solutions. For some organizations, it can be more efficient and economical to move data from one location to another by shipping physical storage devices. This strategy is also more secure than sending the data across the wider internet.
Data format
It's usually relatively straightforward to migrate data in the same format from one location to another. For example, migrating databases from an on-site SQL Server to a cloud-based SQL Server requires no format or schema changes. However, you require an intermediary processing step if you want to transform data into a new format before the data center migration.
Operational outage
When you move data from one place to another, you will face some system downtime or slowdowns. You can schedule your migrations during off-peak hours to minimize impact. Many organizations put off data migration as they can't afford to have any system downtime. However, this approach may increase interruption in the future.
What are the steps in data migration?
Every organization plans their data migration in ways customized to their requirements. We give a broad overview of steps you can follow to make the process more efficient.
Review the source data
Before data migration, you must review and describe the existing data. First look at the data storage format and its current environment. Following this, where applicable, examine the data in a viewer to determine its structure and attributes. You will need to map the structure to the new data system.
Determine the destination
Once the source data has been examined, it’s possible to choose a fitting destination data storage solution based on the source data’s structure and attributes. Sometimes, you need to change the structure, attributes, or even format of the data to fit the new data storage solution. In the case of data integration, you will need to reorganize the source data to fit the specifications of the destination data.
Outline the data migration strategy
Once you define your needs and destination for data migration, you need a plan to execute it. The data migration plan is the roadmap to a successful migration.
To figure out how the data migration process will work, you should make these determinations:
- Systems and data migration tools you require
- Security requirements
- Any data transformation processes
- Costs and human resource requirements
- An approximate timeline of the data migration process
The data migration strategy should also determine the potential impact of the data migration on users. This includes creating contingency plans for operation or a series of communications to alert users of planned outages.
Implement the technical aspects
Before running the data migration process, you must set up the destination environment, including security and permissions. If practical, create a data migration pipeline as code to provide an automated, reusable solution. You can use the code for future, similar migrations, or keep it as a record for documented proof of the process. The codified pipeline serves as a living data migration plan.
Test the solution
Testing is essential to reduce risks associated with the data migration process. The type of testing is dependent on the data and solution. For example, you can choose a smaller chunk of the data to test with, dummy data, or even a copy of the live system data. For data integration, ensure that new test data and existing data match up.
Run the data migration
Once the tests are completed successfully, you can schedule and run the data migration. To troubleshoot in case of unexpected events, ensure the right team is available throughout the process—even if it’s running after hours.
After the data migration, examine the live data in its new environment to check for correctness and ensure that the system works as intended. Once the new system is live and running as expected for a given amount of time, you can safely decommission the old environment.
What are some data migration best practices?
Here are some suggestions to make the data migration process more efficient and cost-effective.
Clearly outline the business case
For a data migration project to be successful, the business case for the migration must be clear and warranted.
For example, imagine that users are already running queries on existing databases for the business. The organization has purchased a new data analysis solution, but only three people have been trained on it so far, with training to be rolled out over a year. If they attempt a database migration before training is completed, the organization could face negative business outcomes.
Carefully assess the solution space
A new data solution may require more decision factors than a regular comparative purchasing decision. For instance, when an organization migrates applications to the cloud, they may want to consider containerizing their architectures before they lift and shift. Containerizing would help maximize the benefits of cloud infrastructure. The target solutions for these two different strategies are also completely different.
Clean the data
While it’s not always necessary, it can be good practice to clean the data before migration. This includes tasks like deduplication, removing incomplete data, and removing incorrect data.
Fully document the process
Documenting the data migration project supports audit reporting for cases like acquisitions, mergers, and compliance activities. It's also helpful for capturing internal lessons learned and organizational knowledge.
What are some data migration challenges?
Given the criticality of data in an organization’s setup, data migration is complex and requires careful risk assessment. We give some common challenges next.
Business continuity
Data migrations should be carried out with as little disruption to services as possible. When it isn’t possible to avoid downtimes or slowdowns, plan migration outside of regular business hours. Give users plenty of warning through channels like emails, in-application notifications, and pinned social media posts.
Migration costs
The tools, human resources, new data infrastructure, and cost of decommissioning old data infrastructure all add up when transferring data. Make sure you budget for all aspects before starting the process. It's also important to factor any costs due to loss of productivity or revenue during application downtime. To keep migration impact costs to a minimum, try to limit outages, and ensure all impacted users are aware of the migration in advance.
Data security
Keeping data secure both in transit and in its new environment requires careful planning. You may want to perform complex encryption before transit and create virtual private networks for the transfer process. Thoroughly test and assess the security rules and permissions of the new environment before migration.
New system failures and faults
It's challenging to ensure the success of data migration for all scenarios. Sometimes transferring data may fail or produce unexpected results. In the event of faults and failures, you need a contingency plan. Always have backups so that it’s possible to roll back to the old data systems if you need to.
How can AWS help with your data migration requirements?
Amazon Web Services (AWS) provides an extensive range of solutions to help you in cloud data migration. We help you find and secure the right services and resources to match your requirements, as well as assist with running the process itself.
For example, you can use these data migration services:
- AWS DataSync to securely discover data and migrate to AWS with end-to-end security, simplified planning, and data movement management.
- AWS Direct Connect to create a dedicated network connection to AWS. This way, you can secure your data as it moves between your network and AWS with multiple encryption options.
- Amazon Data Firehose to stream data. You can reliably load real-time streams into data lakes, warehouses, and analytics services.
- AWS Snowcone to deploy edge computing devices. Snowcone devices are small, rugged, and secure. They offer edge computing, data storage, and physical data transfer on the go. They're good options in austere environments with little or no connectivity.
- AWS Transfer Family to easily manage file transfers. You can also modernize your transfer workflows to Amazon Simple Storage Service (Amazon S3) or Amazon Elastic File System (Amazon EFS). You do this within hours and with your existing authentication systems.
Get started with data migration on AWS by creating an account today.
