As businesses grow, business requirements and needs change. At some point, organizations often outgrow their existing database and must switch to one with better performance, requiring a data migration. Data migration refers to the movement of data from a source location to a new target location. Embarking on a data migration project is rigorous, as it involves more complexity than most IT tasks, involves hosting critical applications and data, and requires careful planning and skilled migration personnel and tools. To stress how challenging migration projects are, Gartner reports that 83% of migration projects fail, and over 50% exceed their budgets.
To avoid these pitfalls, it’s important to create a data migration strategy that acts as a blueprint and informs the entire migration process. Database migration strategies differ depending on the need for migration, costs, business size, downtime tolerance, and more. For example, the migration approach implemented by organization A looking to migrate all data from a legacy on-premise database to the cloud differs from organization B migrating between two cloud databases.
This article discusses the factors that ensure a smooth and successful database migration.
Defining Your Purpose: Why Migrate?
Database migrations occur for various reasons, and defining the reasons helps in planning the steps in your migration strategy. Some of these reasons are:
- Cut costs: An organization may migrate from an on-premise database to a cloud database to harness the benefits of the cloud, like reduced infrastructure costs, enhanced scalability, and availability.
- Consolidation: Organizations may want to consolidate multiple databases into a single, centralized database system for easier management and maintenance, which also helps in achieving a unified view for business decision making.
- Scalability: As businesses evolve, they may need to scale their data infrastructure to meet increasing demands. Migrating to a more scalable database system can help accommodate the growing data volume and traffic. This may also require better storage and processing systems that demand a new database system, influencing data migration from existing databases.
- Security and compliance: Organizations may need to adhere to certain security and compliance regulations that the current database system cannot support. Migrating to a more secure and compliant database system can help organizations meet these requirements.
- Retirement: Old, legacy databases may not keep up with business requirements, hence the need to shut them down and migrate the data to a new database.
Framing the Problem: Types of Database Migrations
When choosing a migration strategy, businesses face the challenges of maintaining a budget, ensuring no data loss, and minimal service downtime. There are various types of data migration, some of which may include moving between databases.
In a database migration, data moves between two database systems, whether upgrading to a newer version of the same database system or moving to a new database system. Database migration can be challenging when the source and target systems use different structures and schemas for their data. Before embarking on database migration, evaluation and testing of the database size, security and quality are essential to ensure a successful migration. Database migration sometimes represents the outcome of database replication efforts. Database replication is the workhouse of database migration and involves continuously making snapshots of data from a source database to a target database. In a database migration, source database systems are, in most cases, shut off after the migration process proceeds successfully, unlike a database replication whereby source database systems continue running.
Storage data migration involves transferring data between two data storage mediums. The main driver is to upgrade old systems into better-performing storage technology. For storage migration, data usually undergo transformative processes like cleaning, validation, cloning, and redundancy.
Application data migration is the movement of software applications from one computing environment to another. It may or may not involve making significant changes to the application to help it perform better in the new environment. This type of migration becomes challenging when the target and source infrastructures have distinct data formats and application structures. Hence one crucial factor that informs the success of an application migration is ensuring communication between both systems to ensure data integrity. Application migration may involve databases holding the software application or lifting only the application.
Partial vs. Complete Database Migrations
Database migrations can be complete, which involves defining the entire dataset for transfer, or partial that involves transferring only a subset of the dataset.
Heterogeneous vs. Homogeneous Database Migrations
For heterogeneous database migrations, the source and target database systems are different with different providers using different schema, datatype, or database code. These differences make a heterogeneous database migration a two-step process, with the first step involving a schema and code conversion tool. For example, migrating an OLAP database on Oracle to Alibaba Cloud ApsaraDB for RDS MySQL will need table transformations to ensure a successful migration.
Homogeneous database migration occurs between systems with the same database systems and providers, for instance, migrations from a MySQL database engine to another MySQL data engine.
Big-Bang vs. Trickle Database Migration
A big-bang database migration involves migrating an entire database to a new system in a single operation. This strategy requires intense preparation because any error or failure affects the complete dataset. Also, because this migration strategy occurs in a short time frame, it causes downtime. Hence the migration strategy must account for downtime tolerance in the migration plan so that business operations remain largely unaffected.
Trickle migration occurs in small data amounts. One can say this approach is several mini-migrations over a longer time. This migration strategy involves breaking down the data into smaller subsets and migrating each chunk separately at its timeline. A key benefit to this migration strategy is that a failure or error in any of these mini-migration chunks only affects a subset of the data, not the entire dataset. Additionally, with subsequent mini-migrations, data engineers learn and improve the migration process. This strategy ensures minimal to zero downtime and manageable failure recovery.
Determining Your Downtime Tolerance
One main factor to consider during pre-migration planning is the amount of downtime your system can handle. In a database migration, database operations – especially with high database traffic — can cause longer downtime. Data engineers and all responsible in the migration planning phase need to determine its downtime tolerance by considering dependent factors like:
- Business needs: The impact of downtime on the business and its operations should be considered. For example, if the organization relies heavily on its database system for real-time transactions, any downtime may result in lost revenue or customer dissatisfaction. Internal operations can also experience a disruption that should be taken into account.
- Size of the database and tables: A voluminous DB requires more time for transfers (for a big bang), and more mini-migrations (for a trickle approach), hence the planning phase should consider this in determining whether a one-time (big bang transfer) or migration in multiple mini-migrations is ideal.
- Database traffic and peak times: Businesses can ensure minimal downtime by determining their database traffic and when its database services are in peak use and schedule migrations away from such periods.
- Need for refactoring or app changes to code: Making changes or refactoring code to the database systems means more time allocated to the migration process, so data engineers must carefully factor this into pre-planning for the migration process.
Choosing Your Database Migration System
Data migration involves the movement and transfer of data from a source to a target location. It mirrors an ETL process, with most migration processes having a transformation and loading process between the source and destination database.
Data migration tools help extract, transform and move data between databases or applications. These tools could be cloud-based, on-premises, or self-scripted, each possessing different levels of flexibility, reliance, security, pricing, and performance at scale. Choosing a cloud migration tool depends on pricing, reliability, data source and destination, and security measures.
For on-premise migration tools, organizations enjoy the flexibility of building their migration stack while ensuring compliance with data protection laws that prohibit migrating workloads over the cloud. However, on-premise migration tools must ensure continuous availability by providing backups in case of any failures during the migration process. Cloud migration tools are easily scalable and dynamic, rising to changing data requirements and are heavily reliable due to their redundant architectures.
Data integration platforms like StreamSets enable easy migration across any environment with dynamic data pipelines by providing a centralized platform that can connect to various sources and destinations, including cloud-based applications, on-premises systems, and legacy databases. That platform provides support for various use cases including CDC, batch, and streaming, which enables organizations to unlock the value of their data without ceding control. The no-code drag-and-drop interface has built-in features to automatically detect data drift, schema changes, and semantics, ensuring a smooth migration between sources and destinations. StreamSets pipelines also use CDC for decentralized systems to perform operations that help synchronize data to ensure data consistency during and after migrations.
Data Migration Checklist
Data migration is challenging, and approaching it without careful planning leads to data loss, inconsistent data, and service downtime. Before embarking on the process, it is crucial to employ a migration best practice of using a migration checklist to confirm readiness. Here are some things to check for before starting the migration process:
- Adequate knowledge of data: Before embarking on the migration, it is vital to know about the data in your system. What volume is present in what formats? What relationships exist between the data? What schema does your source database use? Does data need to transform before loading into the target database?
- Ensure data is of high quality: Engineers can use the migration process to improve data quality.
- Have proficient migration talent: Migration depends on the combined effort of ETL specialists, business and system analysts and efficient migration tools. Each specialist contributes to planning a robust migration strategy that covers business and technological requirements.
- Testing and validation: Rigorous, continuous testing is crucial before the migration process as it helps the migration team detect and fix any issues that may arise during migration. Testing helps ensure that the business logic in the source and target system are balanced and that data integrity and functionality in the new system remain the same as that of the source database. Migration tools should test for functionality, response times, scalability, security, and resilience to data drift.
- Existence of backup and fallback processes: Data loss and disruption are two common issues that plague the migration process. Before proceeding, both source and target systems should be configured for high availability and disaster recovery. Configuration for high availability may involve creating failover replicas to ensure and SQL queries for disaster recovery .
- Standardized data: Standardized data ensures data consistency after migration and ensures that the data types and formats of the source and destination database are the same.
A satisfactory completion of all the tasks on the migration checklist means the migration process can proceed. Data migration typically follows the ETL process:
- Extraction: This first step involves extracting the required data from the database system.
- Transformation: This step transforms the source database data into one compatible with the target system. It includes adding/deleting fields, changing data formats, structure or types, enrichment, deletion and validating the data against set guidelines to ensure only high-quality data moves to the next step. ETL tools or python scripts can help with this step.
- Loading: This step involves loading the newly transformed data into the new database in small increments (batches) or at once.
- Testing and validation: After loading all the data into the new database, the target system needs testing to ensure the database migration is complete and data is consistent, performs as expected with all the data, without any drop in performance or data errors.
- Source database deletion (if required): After testing and validating the functionality of the new database, organizations may decommission and shut down the old database. This step is optional as some organizations may decide to maintain both the legacy and new database systems.
Recapping Database Migration Strategies
Data migration is demanding. The process involves many moving parts and a strategy to achieve success. Your data migration strategy will differ depending on the motivation for migration and should consider factors like database size, source, and schema compatibility, and system downtime tolerance to help plan for costs and migration approach. Large organizations, for example, may utilize the more high-priced, but lower downtime trickle migration approach, whereby data transfers occur in small amounts from a source database to a target database.
Data Integration platforms like StreamSets can help reduce the complexity of performing data migrations by allowing organizations to build dynamic, reusable pipelines. As a schema-agnostic platform, engineers save time and costs involved with changing schema as StreamSets offers automatic schema migration with multi-table updates and automatic drift detection built-in with the migration pipelines.
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