The data ecosystem of an enterprise comprises a variety of applications. Over time, a business may decide to migrate from an existing database to save costs, enhance reliability, achieve scalability, or any other objective. This process of moving data from one place to another is known as database migration.
Even though they are essential, data migration projects can prove to be very complex. Data migration requires downtime, which may lead to interruption in data management operations. This is why it is important to understand the risks and best practices of DB migration, and the tools that can help perform a smooth process.
What is Database Migration: Concept and Definition
DB migration is the process of moving data from one or more source platforms to another target database. There are several reasons for migrating from one database to another. For example, a business might want to save resources by switching to a cloud-based database. Similarly, another organization could move because they find a particular database more suitable for their unique business needs. Hence, it is vital to have an efficient database migration strategy to avoid trouble during the data migration process.
Benefits of Database Migration
Some of the popular reasons for choosing database migration are:
- Database migration is important because it helps to save money
- The benefit of database migration is that it helps move data from an outdated legacy system to a modernized software
- Database migration helps to unify disparate data so that it is accessible by different systems
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 data include:
- Data Loss: The most common issue firms face is the loss of data 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.
- Data Security: Data is a business’s most valuable asset. Therefore, its security is of utmost importance. Before the DB migration process occurs, data encryption should be a top priority.
- Difficulty during planning: Large companies usually have various disparate databases in different departments of the companies. During the planning stage of database migration, locating these databases and planning how to convert all schemas and normalize data is a common challenge.
- Migration strategy: A common question asked is how to do DB migration. Companies miss out on some crucial aspects and end up using a database migration strategy that is not suitable for their company. Therefore, It is necessary to conduct ample research before DB migration takes place.
Why Use Database Migration?
The common reasons for using database migration are:
- Upgrading to the latest version of the database software to improve security and compliance
- Moving existing data to a new database to reduce cost, improve performance, and achieve scalability
- Moving from an on-premise database to a cloud-based database for better scalability and lower costs
- Merge data from several databases into a single database for a unified data view post-merger
How To Do Database Migrations
How does DB migration work? The process of 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 important to have an appropriate database migration strategy along with the right DB migration tools to make the process more efficient.
Let’s take a look at the different steps to understand how to do database migration:
1. Understanding the Source Database
An important database migration step to understand is that 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 will determine the scope of your migration project. This will also determine the amount of 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 are migrating data between different databases, such as an SQL database to an Oracle one, you will need schema conversion capabilities to execute your DB migration project successfully.
2. Assessing the Data
This step involves a more granular assessment of the data you want to migrate. You would want 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 that involve migrating data 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 towards 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 that take place 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.
Expedite Database Migration with Astera Centerprise
Migrations that are not completed on time can 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 while ensuring data quality and minimum downtime.
Astera Centerprise is an end-to-end data management solution that is designed to cater to the complex needs of businesses. When you need to perform a database migration, Centerprise can help you with:
- Parallel processing engine and high-availability feature that promises you 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-premise and cloud-based databases to cater to any data migration use-case
- Drag-and-drop mapping to perform complex data transformations in a code-free manner