Data migration is the process of moving data from one storage device to another. And while the premise is simple, the process can be complex. When migrating data, database or application logic may need to be re-executed. This can include reformatting or transforming data, changing the database schema, or refactoring database stored procedures.
Data migration is often required when an organization moves data to a modern database, transfers it from an older storage solution that is no longer supported, or migrates it from an on-premises solution to a cloud-hosted solution. Another use case is big data migration — migrating large volumes of data to improve availability for other applications that need to access it.
It is important to ensure the integrity and security of data during the data migration process. Therefore, developing a robust data migration plan requires careful analysis and selection of an appropriate data migration plan. Choosing the right approach and migration tool can be the difference between a smooth migration and a migration with bugs, data integrity issues, and potential security issues.
What is Data Migration?
Data migration is the process of transferring data from one data storage system to another and also between data formats and applications. It also involves data transfers between different data formats and applications.
The data migration process also includes data preparation, extraction, and transformation. It is usually conducted when introducing new systems and processes in an organization.
The following are some common scenarios that require data migration:
- Legacy software upgrade and replacement
- Installation of new systems to coexist and augment existing applications sharing the same dataset
- Replacement, upgrade, and expansion of storage systems and equipment
- Firms moving from a local storage system to a cloud-based system to optimize operations
- Website consolidation
- Data center relocation
- Infrastructure maintenance
- Switching to centralized databases to attain interoperability
- Consolidation of information systems
You can choose among several options for transferring data from a local data center to the cloud, but broadly speaking, they fall into two categories:
- Online migration, in which data moves across the Internet or a private or dedicated WAN connection.
- Offline migration, in which data is transferred via a storage appliance that’s physically shipped between its data center of origin and the target cloud storage location.
The best option for your specific data migration project depends on how much data you need to move. It also depends on how quickly the migration must be accomplished, the types of workloads involved, and your security requirements.
Data Migration Process
The data migration process should be well-planned, seamless, and efficient to ensure it does not result in a protracted process or go over budget. It involves the following steps in the planning, migration, and post-migration phases:
The data migration process can also follow the ETL process:
- Extraction of data
- Transformation of data
- Loading data
ETL tools can manage the complexities of the data migration process from processing huge datasets to profiling and integrating multiple application platforms. The data migration process remains the same whether a big bang approach or a trickle approach is adopted.
Big Bang Data Migration Approach
The big bang data migration approach moves all data in one single operation from the current environment to the target environment. It is fast, less complex, and also less costly. Its implementation will mean all systems will be down and unavailable for users during the migration.
Hence, it should be conducted during public holidays or periods where users are not expected to use the system.
The advantages of the above approach are offset by the risk of an expensive failure due to big data, which can overwhelm the network during transmission. Because of such risk, the Big Bang approach is more suitable for small companies with smaller amounts of data or for operations or projects where the migration involves a small amount of data.
Furthermore, it should not be used on systems that cannot sustain any downtime.
Trickle Data Migration Approach
The trickle data migration approach is a phased approach to data migration. Trickle data migration breaks down the migration process into sub-processes where data is transferred in small increments. The old system remains operational and runs parallel with the migration. The advantage is that there is no downtime in the live system, and it is less susceptible to errors and unexpected failures.
However, on the downside, the iterative nature of the process makes it more complex, and it takes longer to complete. During the whole process, data should be synchronized between the old system and the new environment. The trickle migration process is ideal for big data organizations that cannot afford any downtime to their system.
Steps
- Pre-migration planning: This involves the evaluation of existing data sets for stability. An analysis of the source and target system should be carried out. Data standards should also be set to spot any potential data problems. Decisions on whether to use the Big Bang or trickle approaches are also made at the pre-migration planning stage. More crucially, it is where migration budgets, timelines, schedules, and deadlines are set.
- Data inspection: This stage involves inspecting the scope of the data in terms of quality, anomalies, or any possible conflicts and duplications. Software application tools can be used to clean the data if the volume warrants it.
- Data backup: Data backup guards against any migration failure that can lead to data loss. It is a prudent measure that eliminates the risk of data loss.
- Migration process design: The migration process stage stipulates the migration testing procedures, acceptance criteria, and other personnel responsibilities. Hiring an ETL developer or data engineer to take charge of the process is also part of this stage.
- Execute and validate – This is where the execution of the migration process starts. The extraction, transformation, and loading (ETL) processes also go live at this stage. It is essential to monitor and validate the process to see if there is any sign of failure and downtime to the old system if the trickle approach is selected. Continuous communication with business units is also paramount during the migration process. The migration process should be validated to see if it has been executed as per set guidelines and ensure data migrated to the new environment is complete and viable for business use.
- Decommission and monitor – A post-migration step in which the old system is shut down and decommissioned.
Lift and Shift
The lift and shift migration moves an application and its data to the cloud with minimal or no changes to the application architecture, authentication mechanisms, or data flow. When no change is required, the application can be “lifted” as-is from the source environment and “shifted” to a new location.
A lift and shift migration should be planned, considering the application’s network, computing, and storage requirements. It involves mapping from the available resources in the source infrastructure to the cloud provider’s resources. Most cloud vendors offer on-the-fly upgrades to ensure customers can start with a smaller product and scale as needed.
Types of Data Migration
There are six types of data migration, which are:
Storage Migration
Storage migration is where a business migrates data from one storage location to another. It means moving data from one physical medium to another.
A common reason for storage migration is the upgrading of storage equipment to more sophisticated modern storage equipment. Hence, it encompasses movement from paper to digital, tapes to hard disk drives (HDD), HDD to solid-state drives, and hardware-based storage to virtual-based (cloud) storage.
The movement is not driven by a lack of space but rather a desire to upgrade storage technology. It normally does not alter the content or format of data. During storage migration, certain steps such as data validation, cloning, and data cleaning and redundancy can be carried out.
Cloud Migration
Cloud migration concerns the movement of data or applications from an on-premises location to the cloud or from one cloud environment to another. It is, in essence, a specific storage migration. IT experts continue to witness an increase in cloud migration and forecast that the majority of major corporations will be operating on the cloud before the end of the decade ending 2030.
Application Migration
Application migration occurs when an organization goes through a change in application software or changes an application vendor. This migration requires moving data from one computing environment to another. A new application platform may require radical transformation due to new application interactions after the migration.
The major challenge comes from the old and target infrastructures having distinctive data models and using different data formats. Application programming interfaces (APIs) can be provided by vendors to protect data integrity. Vendor web interfaces may be scripted to facilitate data migration.
Business Process Migration
Business process migration requires the movement of business applications and data on business processes and metrics to a new environment. The metrics can include customer, product, and operational information. The migration is commonly instigated by business optimization and reorganization and mergers and acquisitions (M&A).
Such business combinations are necessitated by the need to enter new markets and remain competitive.
Data Center Migration
Data center migration relates to the migration of data center infrastructure to a new physical location or the movement of data from the old data center infrastructure to new infrastructure equipment at the same physical location. A data center houses the data storage infrastructure, which maintains the organization’s critical applications.
It consists of servers, network routers, switches, computers, storage devices, and related data equipment.
Database Migration
Databases are data storage media where data is structured in an organized way. Databases are managed through database management systems (DBMS). Hence, database migration involves moving from one DBMS to another or upgrading from the current version of a DBMS to the latest version of the same DBMS.
The former is more challenging especially if the source system and the target system use different data structures.
Note that a single data migration process can involve different types.
Data Migration Challenges
Because the data migration process involves many moving parts and large amounts of data, a number of issues can interfere with the process. Below are some of them and their impact on the migration process.
Risk of Data Loss or Corruption
During the migration process, organizations should aim to minimize the risk of data loss or corruption. Data may be lost due to various reasons such as incomplete or inaccurate transfer, system incompatibility, human error, etc. This can cause financial losses, hurt an organization’s reputation, and result in compliance violations.
Risk of Business Disruption
Organizations typically don’t want to stop production work during the migration process, so users don’t face downtime and can ensure that all systems are up and running. However, this can be challenging to achieve. Another risk is that changes to data during the migration process will lead to system inconsistencies and inaccurate data.
Risk of Exposure
The possibility of a data breach is a serious risk in the migration process. In the process of data migration, the system and the data itself become more vulnerable. By exploiting vulnerabilities in the data transfer method or in target storage system, an attacker could corrupt, steal, or tamper with data in transit, resulting in a failed, incomplete, or corrupt migration.
Best Practices for Successful Data Migrations
Have a Solid Data Backup and Protection Plan
Consider a scenario in which the migration is not completed correctly. Back up your data regularly, and use tools and techniques to protect your data from various error scenarios. This is also useful if a file gets corrupted for unknown reasons during the migration or if some data is missing or incomplete.
Carefully map your data to destinations to allow team members to see exactly where the data came from, where, how, and when.
Explore and Assess the Source
Before migrating data, you need to know what you are migrating and how it will fit your target system. Find out how much data is being collected and what that data looks like.
There can be many data fields, some of which do not need to be mapped to the target system. Your source may have missing data fields that need to be pulled from somewhere else to fill in the blanks. Ask yourself what needs to be migrated, what needs to be left behind, and what is missing.
Omitting this source review step can make the data migration process expensive and time-consuming. To make matters worse, organizations can experience serious flaws due to data mapping issues, halting a migration completely.
Audit and Document Processes
Complete documentation of a data migration process is critical for compliance in regulated industries. In some industries, regulators may require evidence that appropriate or reasonable controls are in place over sensitive data, such as financial or medical information.
The documentation should not only provide evidence everything went right, but it will also help you identify areas for improvement for your next migration.
Test and Validate Migrated Data
After a successful migration, validate that all data is where it should be. Clean out old data and check that permissions are applied correctly. It is a good idea to back up the old legacy system so that if the new one goes offline, you can access it from another secure location.
Data Migration Software
Building out data migration tools from scratch, and coding them by hand, is challenging and incredibly time-consuming. Data tools that simplify migration are more efficient and cost-effective. When you start your search for a software solution, look for these factors in a vendor:
- Scalability: What are the data limits for the software, and will data needs exceed them in the foreseeable future?
- Connectivity: Does the solution support the systems and software you currently use?
- Speed: How quickly can processing occur on the platform?
- Security: Take time investigating a software platform’s security measures. Your data is one of your most valuable resources, and it must remain protected.
Data migration vs. data conversion: What’s the difference?
To have a clearer understanding of what data migration means, it’s important to know what data conversion is and how it relates to data migration. Often, there is confusion around whether an activity or project is data conversion or data migration because, by definition, data migration includes data conversion.
However, data conversion is just one aspect of data migration, so the two terms cannot be used interchangeably.
Data migration means moving data from one place to another, whereas data conversion means transforming data from one format to another. The following comparison highlights more of the differences and similarities between data migration and data conversion.
| Data migration | Data conversion |
|---|---|
| The data is moved to a new data center, location, system, or environment. | The data is moved to a new application. The data center, system, or environment may remain the same. |
| The format of the data may remain the same. | The format of the data is transformed. |
| The process consists of planning, implementation, and validation. | The process consists of extraction, transformation, and loading. |
| Data migration often includes data conversion, but data conversion is not always required. | Data conversion is often one of the first steps in data migration, but data migration can happen without data conversion. |
Data migration vs. data integration
Data integration is another term often confused with data migration. It refers to the process of combining data residing at different sources to provide users with a unified view of all the data. Integrating data from multiple sources is essential for data analytics.
Examples of data integration include data warehouses, data lakes, and NetApp® FabricPools, which automate data tiering between on-premise data centers and clouds or automatically tier data between AWS EBS block storage and AWS S3 object stores.
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