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 place to another is known as database migration.
Even though they are essential, data migration projects can be very complex. Data migration requires downtime, which may lead to interruption in data management operations. This is why it is important to understand DB migration’s risks and best practices and the tools that can help perform a smooth process.
What is Database Migration?
Database Migration is the process of transferring data from one or multiple source databases to one or more target databases. This is typically done using a database migration service or 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 save resources by switching to a cloud-based database. Similarly, another organization could move because they find a particular database 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:
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.
Cost Optimization: By migrating to the cloud, businesses can avoid significant upfront investments in hardware, infrastructure, and maintenance. Instead, they can leverage pay-as-you-go models, paying only for the resources they use. Cloud databases also offer cost-effective storage and computing options, allowing organizations to optimize their data storage and processing costs. Furthermore, cloud services often include maintenance, updates, and security measures, reducing the need for in-house IT resources and associated costs.
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 Challenges
DB migration has been a common practice for years. However, that does not change 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 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.
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 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 use a database migration strategy that is not suitable for their company. Therefore, It is necessary to conduct ample research before DB migration occurs.
Why Use Database Migration?
The common reasons for migrating from one database to another 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
Source: Medium
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.
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 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.
Expedite Database Migration with Astera Centerprise
Data migration processes that are not completed on time can prove costly, both in terms of revenue and reputation. An enterprise-grade data migration tool can help your business automate the repetitive tasks associated with data migration while strengthening data quality management.
Astera Centerprise is an end-to-end data management solution 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 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 without writing a single line of code
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