What is Data Migration: Plan, Process, & Checklist

Moving data from one location to another is not as simple as copying and pasting. It becomes even more complex when dealing with large amounts of data. Unfortunately, many organizations underestimate the challenges associated with data migration, leading to increased time and costs. According to Oracle research, over 80% of data migration projects exceeded budget and encountered unexpected difficulties or failures.

Therefore, to ensure a successful migration, following a well-defined data migration plan is crucial. In this article, you’ll explore various types of data migration and a checklist to help you navigate the process effortlessly.

What is Data Migration?

Data migration is the process of transferring data from one system, storage, or format to another. It is commonly used in various scenarios to ensure seamless data transfer and enable efficient data management.

Here are specific cases when data migration is needed:

• Moving data to a new storage system or infrastructure, such as transitioning from on-premises to cloud-based services,
• Upgrading applications or database management systems,
• Centralizing data to facilitate interoperability,
• Transferring data during a company merger or data center relocation.

Types of Data Migration

Every data migration project is unique and can vary based on the specific systems and data involved. However, it can be broadly classified into the following five categories:

Database Migration

This type of data migration involves transferring large amounts of data to an updated or different database engine or management system. As the data format can vary among database systems, a transformation process may be required to ensure compatibility.

Before migrating, you must ensure the target database has proper capacity. Additionally, testing should be performed to make sure that there won’t be an impact on the existing applications that use the database.

Storage Migration 

Storage migration is the process of transferring data from one storage device to a new repository. For instance, it could be moving data from a hard disk to a solid-state drive.

During a storage migration, the data typically remains unchanged. The objective is to upgrade to more advanced technology that offers improved scalability, cost-effectiveness, and faster data processing capabilities.

Cloud Migration 

Cloud migration involves moving data or applications to a cloud computing environment like a cloud data warehouse. It specifically refers to transferring data from a private, on-premises data center to the cloud or between different cloud environments. This can offer various benefits, including scalability, flexibility, accessibility, and potential cost savings.

💡Note: Here is a glimpse into the most widely used cloud migration strategies: Rehost, Relocate, Repurchase, Replatform, Re-Architect, Retain, and Retire. For more detailed insights on these strategies, refer to this informative article.

Application Migration

Application migration refers to moving a software application, such as an ERP or CRM system, to a new computing environment. The process may require both database and storage migrations.

As part of the migration process, the database that the application uses may need to be relocated and even transformed in format to fit a new data model.

Business Process Migration

Data often needs to be transformed as it moves from one data model to another. This type of migration involves the transfer of databases and applications that contain information related to customers, products, and operations.

You may opt for this migration to streamline business processes, introduce new products or services, or complete a merger or acquisition.

The Essential Data Migration Checklist

Here is a comprehensive checklist that covers various aspects of the data migration process:

Evaluate Data Sources

This phase requires a comprehensive understanding of the data that needs to be migrated, including its formats, volume, and quality. The primary objective is to assess data sources, identify any issues that may arise, and plan for their resolution.

Identify Data Sources

• Make a comprehensive list of all data sources involved in the migration, such as databases, applications, or files.
• Document information like data types, formats, and custom fields or attributes associated with each source.

Data Cleanup

• Clean up and standardize the data to ensure accuracy, especially if it is coming from multiple sources.
• Address any data inconsistencies, missing values, or duplicate records before migration.
• Establish data quality rules and validation processes to ensure data integrity post-migration.
• Run data quality checks on each source to identify and address errors or gaps.

Conduct Data Profiling

• Analyze datasets to identify patterns, anomalies, and structures.
• Evaluate data dependencies and interrelationships between sources to ensure their integrity throughout migration.
• Identify and exclude any historical or redundant data segments that are unnecessary for the migration.

Data Mapping

• Establish clear mapping rules for each data element, such as field names, formats, and required transformations.
• Create a document outlining the mapping from the source to the target system. Use it as a reference during the migration process.
• Develop test cases to validate the accuracy of the data post-migration based on the defined mappings.

Data Migration Planning

This phase involves developing an overall data migration plan and allocating resources accordingly.

Define Project Objectives 

• Identify the reason behind the data migration, and list the expected outcomes you hope to accomplish.
• Establish specific performance benchmarks for the migration process, such as data transfer speed, minimal downtime, or successful data validation rates.

Outline Timelines and Milestones

• Create a detailed plan with timelines for the migration project.
• Define various project phases, starting from data assessment to testing and validation.
• Allocate resources effectively and assign responsibilities for each phase.
• Plan for potential delays in case of unforeseen issues or complications.

Data Backup and Security

• Ensure that appropriate data backups are in place to prevent data loss during the migration.
• Incorporate strong security measures such as encryption and multi-factor authentication to protect sensitive data throughout migration.

Select Migration Strategy

• Determine which migration approach, either one-time or incremental, aligns best with the requirements of your organization.
• Based on data volume and complexity, choose between various migration approaches, such as big bang (migrating all data at once), phased (migrating section by section), or trickle (continuous, incremental migration).

Pre-Migration Validation

• Before the actual migration, test your data movement process to ensure it can handle the expected data volume.
• Establish a test environment that closely replicates the production environment to assess the migration process accurately.
• Use sample data to perform rigorous testing and address any potential challenges.

Execute Migration

This step involves transferring the data into the target system. You can utilize data migration tools or techniques to transfer the data securely.

The duration of the migration process will vary depending on the data volume and the migration approach used. After the data is loaded into the target repository, you can utilize monitoring tools to track the progress, identify potential bottlenecks, and ensure the timely completion of the migration process.

Real-time Monitoring

• Implement alerts or triggers for critical errors or delays that require immediate attention.
• Monitor for any data integrity issues or errors that may arise during the migration and address them to maintain data quality.

Logging

• Establish logging systems to track the data migration effectively.
• Continuously monitor critical metrics such as data transfer rates, system downtime, and error logs to identify potential issues.

Test and Validate

In this phase, testing is conducted to verify the accuracy and completeness of the migrated data, ensuring that it matches the source data without any inconsistencies.

Post-Migration Validation

• Validate the migrated data by cross-checking a sample set from the source system.
• Conduct user acceptance tests to help ensure that the data meets all the required business needs and functions correctly in the new system.
• Run queries on relevant fields and compare results between the source and target systems to validate the data integrity after migration.

Documentation

• Maintain a comprehensive log of all data transfer processes, documenting any errors or issues that arise during the migration.
• Record the data mapping process and document the transformations implemented to ensure a successful migration.

Challenges in Data Migration Process You Might Face

Here are a few common migration risks you may encounter and the solutions to overcome them:

Data Loss

Challenge: The risk of data loss during migration is a significant concern. This can pose severe consequences, including operational disruptions and financial losses.

‍Solution: Perform an overall data backup before migration to create a secure copy of your critical data, ensuring you can restore it if any loss occurs. Furthermore, you should implement a test migration in a controlled environment to identify risks and address any issues before executing the migration.

Extended Downtime

Challenge: Migrating large volumes of data can lead to downtime and performance issues, affecting the availability and responsiveness of the system.

‍Solution: Plan the migration during low-usage periods (off-peak hours) to minimize impact. By using incremental migration techniques, you can transfer data in smaller batches and gradually reduce system downtime. 

Security Concerns

Challenge: Insufficient data protection during migration can make sensitive information vulnerable to security risks, which could result in potential data breaches.

‍Solution: Encrypt data both in transit and at rest to secure sensitive information from unauthorized access during the migration process. Utilize secure transfer protocols, such as SSL/TLS, to ensure data integrity and confidentiality as it moves between systems.

Data Mapping Complexity

Challenge: Mapping data from source to destination systems could be complex, especially when dealing with diverse systems and formats.

‍Solution: Create a detailed plan that clearly defines how each data field in the source system maps to the destination system. You can leverage data mapping tools to streamline the process efficiently.

Data Quality Issues

Challenge: Your business collects large volumes of data, but not all of it is of high quality. Migrating missing values or inconsistencies may lead to poor data.

‍Solution: To prevent data quality issues, perform data cleansing before migration. Implement data profiling to identify inconsistencies and use data validation rules to ensure accuracy and completeness.

Streamline Your Data Migration Journey Using Airbyte

Now that you have a checklist to implement data migration, it's essential to address the major challenge that lies ahead. Data often resides in multiple systems with varying data formats and structures, making migration extremely challenging. Therefore, it is crucial to integrate data from various sources into a centralized location. This is where Airbyte, a fully-fledged data integration platform, can be helpful.

With Airbyte, you can easily integrate data from different systems, databases, APIs, and file formats into a centralized repository, ensuring a smooth and efficient migration process.

Let’s explore the key features of Airbyte:

‍Custom Connectors‍

Airbyte offers a vast library of over 350+ pre-built connectors that enable you to seamlessly integrate various data sources, ensuring efficient and secure data transfer without the risk of leakage.

Furthermore, if you don’t find the desired connector, Airbyte empowers you with even greater flexibility through its Connector Development Kit (CDK). With the CDK, you can quickly build custom connectors in less than 30 minutes.

‍Transformations‍

Airbyte adopts the ELT (Extract, Load, Transform) approach, which involves loading data into the target system prior to transformation. However, it allows you to seamlessly integrate with dbt (data build tool), empowering you to perform advanced and customized data transformations.

‍Data Security‍

It prioritizes the security and protection of your data by adhering to industry-standard practices. It employs encryption methods to safeguard data in transit and at rest.

Additionally, it incorporates robust access controls and authentication mechanisms, guaranteeing that only authorized users can access and utilize the data.

‍CDC‍

Airbyte's CDC (Change Data Capture) capabilities facilitate seamless synchronization of updated information with your designated target system. 

Wrapping Up

Data migration is a challenging process that requires extensive planning, coordination, and execution. This article has provided insights into different types of data migration, including the planning phase and essential checklist.

By following the recommended steps and utilizing a data integration platform like Airbyte, you can ensure a smooth transition of your data. Sign up today to explore its powerful features.

FAQs

What’s the Most Effective Strategy for Data Migration?

The best approach for data migration involves comprehensive planning, assessment of data sources, and testing to ensure data integrity and minimize disruptions.

‍What are the Risks of Database Migration?‍

The risks of database migration include data loss or corruption, system downtime impacting business operations, and compatibility issues between source and target systems.