March 27th, 2025 
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Database Migration: What It Is and How to Execute It
The data ecosystem of an enterprise comprises a variety of applications. Over time, a business may migrate from an existing database to save costs, enhance reliability, achieve scalability, or any other objective. This process of moving data from one database to another is known as database migration.
Even though they are essential, data migration projects can be very complex. If not appropriately planned, database migration may require downtime, which can interrupt data management operations. This is why it is essential to understand the inherent risks involved in DB migration, the best practices, and the tools that can help perform smooth database migrations.
What is Database Migration?
Database Migration is the process of transferring data, metadata, and schema from one or multiple source databases to one or more target databases. This is typically done using a database migration service or software/tools. Once migration is complete, the dataset from the source databases is fully transferred, possibly with restructuring, to the target databases. Following migration, clients accessing the source databases are redirected to the target ones, and the source databases are deactivated.
There are several reasons for migrating from one database to another. For example, a business might want to create a reliable backup of its existing data as part of a broader strategy to improve resilience and business continuity and minimize downtime. Or maybe they want to save resources by switching to a cloud-based database. Similarly, another organization might want to migrate to another database because they find it more suitable for their unique business needs.
Regardless of the purpose, it is vital to have an efficient database migration strategy to avoid trouble during the data migration process.
Database replication vs. database migration
Speaking of creating a database backup, it can be done in several ways. In fact, database backups are more of a precautionary step taken before the actual migration to ensure that data is protected and can be restored in case of failure. Businesses, therefore, frequently replicate databases and create redundant copies of their data to improve its availability and distribute loads across different systems.
Database replication differs from database migration in that replication is an ongoing process, while migration is typically a one-time or periodic event. Unlike database replication, which keeps the source and replica(s) synchronized over time, database migration establishes a new environment for the data, usually with minimal or no connection to the original database once the move is complete.
The table below summarizes database migration vs. database replication:
| Database Replication | Database Migration | |
| Purpose | Maintain multiple copies of data for high availability, fault tolerance, and load balancing | Database migration transfers data from one database to another, often as part of system upgrades or transitions |
| Frequency | Continuous or periodic | One-time or periodic |
| Data Transfer | Ongoing synchronization of changes | Permanent transfer of data |
| Use Cases | High availability, disaster recovery, load balancing, geo-distribution | System upgrades, cloud migration, technology change |
| Data Integrity | Syncing changes in real-time or near-real-time | Ensures accuracy and integrity of transferred data |
| Impact on Source | Source database remains operational | Source database may be deactivated after migration |
| Common Technologies | MySQL Replication, PostgreSQL Streaming Replication, SQL Server Always On | AWS Data Migration Service, Azure Database Migration Service, ETL tools |
What does the modern database migration architecture look like?
Traditional database migration involves manually moving data from one database system to another, typically from on-premises infrastructure to either another on-premises system or a cloud-based solution. This process is resource intensive as it involves a series of manual steps, including data extraction, schema conversion, and data loading, making it prone to errors.
Building on these limitations, the modern database migration architecture has evolved to support more efficient and scalable processes, ensuring minimal downtime and greater flexibility across cloud and on-premises environments. At the heart of the modern database migration architecture is an automated migration engine or a database migration platform, such as Astera, that automates the entire process.
| Traditional Architecture | With Automated Platforms (like Astera) | |
| Automation | Limited, manual scripts required for migration tasks like data extraction, transformation, and loading | Highly automated with drag-and-drop interfaces and built-in automation for tasks such as schema mapping and data transformation |
| Schema mapping | Typically, manual or semi-automated, requiring custom scripts to map schemas between different databases | Fully automated schema mapping, allowing for easy mapping between source and target databases |
| Monitoring | Basic, may need separate tools | Comprehensive, tool-provided |
| Rollbacks | Requires manual intervention, such as restoring backup copies or recreating the source system | Automated workflows for rollbacks, allowing quick restoration to previous states in case of issues during migration |
| Cost | High upfront costs for infrastructure and maintenance, ongoing labor costs | Subscription or usage-based; cost-effective |
| Scalability | Requires manual resource allocation (e.g., adding server capacity or writing custom scripts), making the database migration complex | Resources are dynamically adjusted, handling large data volumes more efficiently during migration |
| Flexibility | Tightly coupled to specific systems and require significant reengineering when adapting to new databases or requirements | Visual interfaces and configuration options allow for quick adaptation to new databases or changing requirements during migration |
| Error handling | Manual error handling is required | Automated error handling |
| Platform-agnostic | Typically, traditional migrations are bound to specific databases or platforms, requiring significant effort to migrate or adapt to other technologies | Modern tools are platform-agnostic offering seamless integration with multiple databases, cloud services, and applications |
Why do businesses migrate databases?
Database migration is fundamentally driven by an organization’s need to adapt to evolving technological, operational, and strategic requirements, such as:
Modernization of technology
When legacy databases no longer support the advanced features or integrations critical in today’s technology ecosystems, businesses migrate to a database that enables enhanced performance, scalability, and compatibility with current technologies.
Infrastructural changes
Organizations may decide they must shift from on-premises systems to cloud-based platforms or move between cloud providers. Both these scenarios necessitate database migration to align the infrastructure with new operational models and reduce dependency on outdated hardware.
Cost Optimization
Over time, the cost of maintaining older database systems becomes prohibitive, particularly if licensing fees or operational expenses are high. Migrating the database in this case aligns IT budgets with business priorities.
By migrating to a cloud database, businesses can avoid significant upfront investments in hardware, infrastructure, and maintenance. The cloud enables businesses to leverage pay-as-you-go models, requiring payments only for the resources used. Cloud databases also offer cost-effective storage and computing options, allowing organizations to optimize their data storage and processing costs. Maintenance, updates, and security measures are bundled as part of the overall solution, further reducing the need for in-house IT resources and associated costs.
Beyond these needs, businesses also undertake database migration to benefit from:
Improved Performance
Database migration improves performance by optimizing hardware and infrastructure, optimizing database design and indexing strategies, segmenting and partitioning data, and utilizing advanced database features. These optimizations result in faster data retrieval, reduced query execution times, and improved overall database performance.
Advanced Features and Functionality
Database migration enables organizations to leverage the advanced features and functionality provided by the new system. This may include support for new data types, improved data analytics, better concurrency control, built-in support for high availability, and disaster recovery.
Data Consolidation
In scenarios where multiple databases exist within an organization, migrating them into a single database helps consolidate data. This consolidation simplifies data management, reduces duplication of data, improves data integrity, and enables better analytics and reporting capabilities across the organization.
Business Continuity and Disaster Recovery
By migrating data to secure infrastructure, businesses minimize the risk of data loss and ensure quick data recovery in the event of an unexpected incident or disaster. Cloud-based data storage and backup solutions provide built-in redundancy, ensuring data availability even in the face of hardware failures or natural disasters. Regular backups and disaster recovery plans, implemented as part of the data migration process, enable organizations to restore critical data and resume operations swiftly, minimizing downtime and potential financial losses.
Database migration strategies and types
Multiple database migration strategies exist, and organizations must conduct thorough due diligence to select the most appropriate one. As a rule of thumb, the choice of strategy should align with the organization’s objectives, technical environment, and tolerance for downtime. Before proceeding with the migration, businesses must evaluate factors such as the complexity of the new database, the compatibility between the source and target systems, and the expected performance improvements.
Common strategies include:
Big Bang database migration
With the Big Bang approach to database migration, businesses aim to transfer all data in a single, one-off event. This type of database migration requires a downtime window during which the old system is taken offline, and the new system is brought online once the migration is complete. While this database migration method minimizes the overall duration of the migration process, it carries significant risks, including potential data loss, unforeseen technical issues, and extended downtime if problems arise.
Phased database migration
When businesses need to transfer data gradually in smaller, more manageable batches, when extended downtime is not an option, they resort to the phased database migration approach. The main advantage of this strategy is that businesses can continue to operate during the migration process. Phased migration offers greater flexibility and lowers risk compared to the Big Bang approach. However, it requires careful planning to synchronize the systems, manage dependencies, and ensure data consistency across both environments.
Phased database migration is also referred to as trickle database migration as the data is moved incrementally over time, allowing organizations to migrate portions of the database while the source system remains operational.
Re-platforming
In scenarios where the goal is to improve performance, scalability, and cost efficiency by leveraging the features of the new platform without undertaking a complete redesign of the database or application, businesses adopt the re-platform strategy. Re-platforming refers to moving a database from one platform to another while making minimal changes to the application’s architecture or code. A common example of re-platforming is migrating from an on-premises database to a managed cloud database with schema optimizations.
Refactoring (Re-architecting)
Refactoring is a more intensive database migration strategy that modifies the database schema, queries, and possibly the application code to align with modern practices and take full advantage of the target platform. A business would typically consider refactoring when migrating from legacy systems to modern databases, such as transitioning from a monolithic on-premises database to a cloud-native distributed database or shifting from relational databases to NoSQL databases.
Lift-and-shift migration
As the name suggests, the entire database is transferred as is from source to target system without any major changes to the schema and data structure. A lift-and-shift database migration can be implemented using either Big Bang or phased approaches, depending on the organization’s requirements and constraints.
Replication-based database migration
Replication-based migration involves moving data continuously from the source database to the target database in near real-time or in scheduled intervals. It is often employed when moving to cloud databases, upgrading database versions, or transitioning to distributed architectures. The approach uses a replication mechanism—such as change data capture (CDC)—to detect changes in the source database and apply them to the target database.
Replication-based migration is also called a zero-downtime database migration because it continuously synchronizes the source and target databases in real time, allowing the source system to remain fully operational throughout the migration process.
How To Do Database Migrations
How does DB migration work? DB migration is a multi-step process that starts with assessing the source system and finishes at testing the migration design and replicating it to the product build. It is essential to have an appropriate database migration strategy and the right DB migration tools to make the process more efficient.
Source: Medium
Let’s take a look at the different steps to understand how to do database migration:
1. Understanding the Source Database
A vital data migration step to understand is the source data that will populate your target database before starting any database migration project. Here are the questions that you should be able to answer about your source database:
- What is the size of the source database? The size and complexity of the database you are trying to migrate to will determine the scope of your migration project. This will also determine the time and computing resources required to transfer the data.
- Does the database contain ‘large’ tables?’ If your source database contains tables that have millions of rows, you might want to use a tool with the capability to load data in parallel.
- What kind of data types will be involved? If you migrate data between different databases, such as an SQL database to an Oracle one, you will need schema conversion capabilities to successfully execute your DB migration project.
2. Assessing the Data
This step involves a more granular assessment of the data you want to migrate. You would like to profile your source data and define data quality rules to remove inconsistencies, duplicate values, or incorrect information. Data profiling at an early stage of migration will help you mitigate the risk of delays, budget overruns, and even complete failures. You will also be able to define data quality rules to validate your data and improve its quality and accuracy, resulting in efficient DB migration.
3. Converting Database Schema
Heterogeneous migrations involving migration between different database engines are relatively more complex than homogenous migrations. While schemas for heterogeneous database migrations can be converted manually, it is often very resource-intensive and time-consuming. Therefore, using a data migration tool with database schema migration conversion capability can help expedite the process and migrate data to the new database.
4. Testing the Migration Build
It’s a good idea to adopt an iterative approach to testing a migration build. You can start with a small subset of your data, profile it, and convert its schema instead of running a full migration exercise at once. This will help you ensure that all mappings, transformations, and data quality rules are working as intended. Once you have tested a subset on your database migration tool, you can increase the data volume gradually and build a single workflow.
5. Executing the Migration
Most companies plan migration projects for when they can afford downtimes, e.g., on weekends or a public holiday. That said, it is now more important than ever before to plan DB migrations to minimize or outright eliminate interruptions to everyday data management processes.
This can be achieved with paid and free database migration tools that offer data synchronization or Change Data Capture (CDC) functionality. Using these tools, you can perform the initial load and then capture any changes during or after the initial load.
Once all the data has been migrated to the new database successfully, a retirement policy needs to be developed for the old database, if required. In addition, systems need to be put into place to validate and monitor the quality of the data transferred to the target database.
Addressing database migration challenges
DB migration has been a common practice for years. However, that does not change the fact that it requires careful consideration due to the complex nature of its data migration steps. Some key challenges companies encounter while migrating their databases include:
Data Loss
The most common issue firms face is data loss during the DB migration. During the planning stage, testing for data loss or data corruption is crucial to verify whether complete data was migrated during the migration process or not. Conducting a detailed inventory of data helps identify sensitive and critical data, which is necessary to ensure everything is accounted for.
Automated database migration tools enable data professionals to compare source and target data, ensuring data integrity and consistency.
Data Security
Data is a business’s one of the most valuable assets; its security is of utmost importance. As data moves between different environments during the DB migration process, its security becomes a challenge. Encrypting data in transit and at rest and limiting access to sensitive data are best practices to ensure data security when migrating a database.
Difficulty during planning
Large companies usually have disparate databases in different departments of the companies. Locating these databases and planning schema conversion and data normalization become a challenge during the initial stages.
The key is to develop a step-by-step plan covering timelines, roles, dependencies, and resource allocations and perform a pilot migration on a small dataset to identify potential issues before full-scale implementation.
Migration strategy
A common question asked is how to do DB migration. Companies miss out on some crucial aspects and use a database migration strategy that does not align with their business goals. Therefore, it is necessary to conduct ample research before DB migration occurs.
Advantages of using automated database migration software
Simplicity and Usability: Reduces the need for specialized coding expertise, making the migration process more accessible to business users and teams.
Speed: Automation shortens migration timelines significantly.
Scalability: Supports migrations involving vast datasets or complex ecosystems with ease.
Error Reduction: Built-in validation and error-handling mechanisms minimize the risks of manual oversight.
Support for Hybrid and Multi-Cloud Setups: Seamlessly handles migrations across on-premises and cloud environments, enabling flexibility in deployment strategies.
Expedite Database Migration with Astera
Data migration processes that exceed timelines prove to be costly, both in terms of revenue and reputation. An enterprise-grade database migration tool can help your business automate the repetitive tasks associated with data migration and data quality management.
Astera is an end-to-end data management solution designed to cater to organizations’ complex data needs with:
- Parallel processing engine and high-availability feature that promise optimal performance with minimal downtime
- Data synchronization capability that helps you capture changed data and save the time and processing power spent on bulk data loads
- Advanced data profiling and quality features that allow you to validate data against custom business rules to minimize errors and inconsistencies
- Support for a range of on-premises and cloud-based databases to cater to any database migration use-case
- Drag-and-drop mapping to perform complex data transformations without writing a single line of code
Are you interested in learning more? Here’s what to do next:
Contact us to discuss your database migration use case.
Authors:
Nida Fatima
Khurram Haider
