When it comes to one of the most critical components of modern data management, data migration plays an important part - it covers tasks like data preparation, extraction, and transformation, all of which are carried out according to a specific migration plan. Organizations often undertake data migration to upgrade their technology, boost productivity, and reduce storage costs.

What is Data Migration?

Data migration involves the transfer of data between different data storage systems, data formats, or computer setups. It comprises an array of tasks, including data preparation, data extraction, and data transformation, all executed in adherence to a particular plan. The specifics of this plan may vary from one organization to another. The overall objective of data migration remains constant: to ensure the secure, efficient, and successful transfer of data to a new application, storage infrastructure, or cloud environment. Data migration projects are typically initiated by organizations as they embrace new systems and operational paradigms, offering a variety of benefits such as enhanced application capabilities, heightened operational efficiency, and substantial reductions in storage-related expenditures.

What is Data Migration? 

What Are the Types of Data Migration?

There are different types of data migrations, including storage migration, database migration, and application migration.

  • Storage Migration: This revolves around the precise relocation of data from one physical storage medium to another, frequently requiring the upgrade of storage equipment to cutting-edge solutions.
  • Database Migration: This centers on the smooth transition of data from source databases to target databases. These efforts often require adjustments to the data's structure or content.
  • Application Migration: This assumes a critical role when the aim is the seamless transfer of data from one application to another. This task can be notably complex due to the inherent disparities in application logic and data management principles.

How to Plan a Data Migration?

Planning a data migration initiative is a process that demands thoroughness and exhaustive preparation from the technical personnel. Before initiating it, following the below steps is crucial:

  • Understanding Data Attributes: Before starting the migration, have a thorough understanding of the data's format, current storage location, and sensitivity. This helps define the desired data format after migration.
  • Defining Project Scope and Resource Allocation: Clearly outline the project's scope, allocate necessary resources, establish a realistic budget, and conduct a detailed analysis of both the source and target systems.
  • Choosing a Data Migration Methodology: Common strategies fall into two categories: the "big bang" approach and the "trickle" method. Select an appropriate data migration strategy that aligns with your organization's unique needs.
  • Thorough Data Preparation: This stage involves rigorous data cleansing, precise data mapping, and flawless data transformation. It's crucial to prioritize the development and integration of robust data security measures.
  • Implementing the Migration Solution: Utilize suitable software tools and technologies to execute the migration. Rigorously test the data migration process to ensure it meets the intended objectives.
  • Conducting User Acceptance Testing (UAT): Involve end-users in the testing phase to ensure that the migrated data aligns with their functional requirements and expectations.
  • Execution and Monitoring: Execute the planned migration, closely monitor the process, and promptly address any unforeseen challenges. After completing the migration, you should conduct a post-migration review to confirm the transfer of all data and the functionality of the new system.

What Are the Challenges of Data Migration?

While data migration offers various benefits, such as improved processing, scalability, and advanced data management features, it also presents several challenges that can impact the efficiency and success of the process. Some of the major challenges of data migration include:

  • Data Gravity and Data Silos: Data gravity occurs when data attracts other data and applications, making it challenging to migrate data from one system to another. Data silos refer to isolated data sources that are not easily accessible or integrated with other systems, requiring complex and time-consuming migration processes.
  • Data Security and Compliance: Data migration involves exposing data to third parties and potentially transferring it to a new system with different security vulnerabilities compared to the legacy system. Ensuring data security and compliance with regulations such as GDPR and HIPAA can be challenging.
  • Data Complexity: The larger the amount of data to be migrated, the more diverse data types, formats, and relationships need to be dealt with. Managing complex data structures and ensuring compatibility during the migration process can be a significant challenge.
  • Data Loss and Corruption: During data migration, there is a risk of data loss or corruption, which can have severe consequences for the organization. Implementing backup and rollback strategies and conducting thorough testing can help mitigate these risks.
  • Integration Problems: Data sources may need to be integrated with other tools and systems to enable data sharing and exchange. Selecting an ineffective data migration strategy can result in losing access to insights generated from the migrated data.
  • User Training: When transitioning to a new system, users may require training on how to use the new system and access the migrated data. Ensuring that users are familiar with the new system and can effectively work with the migrated data is crucial for a successful migration process.

What Are the Best Practices for Data Migration?

If you are looking for a successful outcome for the migration, here are some recommended best practices for data migration that can help you navigate the aforementioned challenges, and enhance your data migration process:

  • Exploring and Assessing the Source: Before initiating the data migration, thoroughly understand the data you're migrating and how it fits into the target system. Assess the volume, variety, and velocity of the data.
  • Understanding Your Data: Carefully examine the data that will be migrated. Identify and remove any unnecessary or outdated data. Determine if there are any regulated data types that require specific security controls for compliance purposes.
  • Choosing the Right Migration Strategy: When considering an appropriate migration strategy for your migration planning, you must gain a prior understanding of the two major migration strategies. "Big bang" migrations are suitable when you can afford a designated downtime window, while "trickle" migrations are preferable for systems that cannot be interrupted. Carefully consider your specific needs to make the selection.
  • Cleaning up and Validating Source Data: Perform a comprehensive audit of the source data before migration. Identify and resolve any issues or inconsistencies. This may involve utilizing software tools and external resources due to the scale of the task.
  • Backing up the Data Before Execution: Prior to initiating the migration, ensure that there are backup resources in place. Test these backups to guarantee their reliability. This ensures that if any issues arise during implementation, you have a fallback option.
  • Sticking to the Plan: Create a detailed migration plan that outlines each step and the personnel involved. Even if the process appears to be proceeding smoothly or encounters unexpected challenges, maintain adherence to the plan. Data migration can be complex and frustrating at times, so be prepared for potential difficulties and follow the established plan.
  • Test and Validate the Migrated Data: After completing the migration, thoroughly test and validate the migrated data to ensure its accuracy and integrity. Verify that the data is functioning as expected in the new system.

The Two Common Data Migration Strategies

To ensure the right selection of the data migration migration strategy, exploring both the big bang and trickle methods becomes essential, as each of them offers some distinct advantages for consideration prior to selection:

  • Big Bang Migration: This strategy involves transferring all data from the existing environment to the new one in a single operation. It is characterized by its high speed and creates a clear distinction between the old and new systems. The main advantages of the big bang method are:
  • Speed: The entire migration process is completed quickly, minimizing user downtime.
  • Clear Transition: Users experience a defined point in time when they can no longer use the old data, and the new system goes live.

However, the big bang method carries risks, as any issues that arise during the process can have significant implications for the business. It is generally suitable for small companies or businesses dealing with small data volumes but not for mission-critical applications that require continuous availability.

  • Trickle Migration: In contrast to the big bang method, trickle migration involves gradually transferring data over time. During this process, the old and new systems run in parallel for a period. The main advantages of the trickle method are:
  • Reduced Risk: Issues can be detected and resolved during the incremental migration, lowering the overall risk compared to the big bang method.
  • Business Continuity: Users can continue working with the old system while the migration takes place, minimizing disruptions.

It is important to note that trickle migration has its downside; it can be more complex and time-consuming, requiring additional efforts and resources to maintain both systems. It is typically suitable for businesses with a higher tolerance for risk, sufficient resources for thorough planning and testing, and a preference for a gradual transition to the new platform.

What Are the Tools for Data Migration?

Utilizing data migration tools to streamline the migration process, ensuring the seamless selection, preparation, extraction, and transformation of data to align with the new storage location. These tools come in three primary categories, which are chosen based on the requirements and circumstances of the data user:

  • On-Premises Tools: These are used for moving data between land-based servers or databases without using the cloud.
  • Cloud-Based Tools: These tools are used for transferring data to large-scale cloud storage systems.
  • Open-Source Tools: Freely available, these tools can be customized to meet specific data migration needs.

Sangfor’s Solutions for Data Migration

Sangfor's robust solutions and tools can ensure a data migration journey with minimal disruption and highest efficiency by simplifying and optimizing the entire process:

  • Sangfor Cloud Migration Tools or Platform (SCMT/ SCMP): This platform supports various migration types, including P2V, V2V, V2C, and C2C. It ensures efficient data replication, encryption, compression, and bandwidth control. With flexible migration strategies and minimal service interruption, it optimizes cost and benefits for any businesses.
  • Sangfor Hyper-Converged Infrastructure (HCI): Leveraging advanced technologies like the CPU throttling algorithm and compression, Sangfor HCI minimizes service interruptions during data migration. It considers key metrics to reduce downtime, making it an efficient choice for smooth migrations.


Data migration makes modern data management easier for any business, as it is capable of completing steps such as data preparation, extraction, and transformation. If you are looking to upgrade technology, enhance productivity, and reduce storage costs, this is a vital process to take forward. Sangfor offers two powerful solutions, Sangfor Cloud Migration Tools and Sangfor HCI, to simplify and optimize data migration, making it an efficient choice for businesses aiming to achieve smooth and interruption-free data migrations.

Frequently Asked Questions

The goals of data migration are to ensure that data is accurately and completely migrated with reduced data downtime and minimum migration costs. Making sure an accurate and complete migration can warrant that all necessary data is successfully transferred and that the data remains usable and accessible in the new location. With reduced data downtime, data can remain available to users during the migration process, which avoids causing disruptions to business operations, while minimizing migration costs can ensure the migration process is cost-effective and does not exceed the budget allocated for the project.

Data migration processes can encounter risky and difficult circumstances, such as "data gravity." Data gravity is the idea that as data grows, it attracts other data to it, making it more difficult to move, and becomes more personalized over time. It can become difficult to accurately and completely migrate data from one location to another, as there may be dependencies and relationships between different sets of data that need to be maintained. Moreover, data migration can be risky due to possible errors or data loss during the migration process, causing significant consequences for the business, such as lost revenue or damaged reputation.

While data migration and data integration are related in certain aspects, they are entirely distinct processes. Data migration is a one-time and one-way process of moving data from one location to another, and data integration is an ongoing process of combining data from different sources and providing users with a unified view of the data.

Data migration is typically used when a business is moving from one system or platform to another. On the other hand, data integration is used to combine data from multiple sources, such as databases, applications, and APIs, into a single, unified view. Data integration can be used to support a variety of business processes, such as data analysis, reporting, and decision-making.

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