Business computer-technology-isometric-icon-server-room-digital-device-set-element-design-pc-laptop_39422-1026

Published on January 21st, 2021 | by Varsha Solanki


Data Migration – What It Is and How to Do It

The data ecosystem of an enterprise includes many applications. Over time, a business may choose to migrate from an existing database to save costs, enhance reliability, accomplish scalability, or any other objective. This cycle of moving data from one place to another is called database migration or DB migration.

Data migration projects can sometimes prove to be very tricky. It requires downtime, which may prompt interruption to database management services. That’s why it is essential to comprehend the risks and best practices associated with database migration and the tools that can help simplify the process.


What is Database Migration?

DB migration is the procedure of moving data from one or more source platforms to another target database. There are a couple of reasons for migrating from one database to another. For instance, a business might want to save resources by switching to a cloud-based database. Likewise, another organization could move since they find a specific database more reasonable for their unique business needs.

Benefits of Database Migration


  • Costs
    • One of the fundamental reasons that organizations migrate databases is to save money. Often organizations will move from an on-premise database to a cloud database. This saves money on infrastructure as well as the manpower and expertise expected to support it.
  • Modernized software
    • Another common reason for migration is moving from an outdated system or legacy systems to a system intended for modern data requirements. In the time of big data, new storage methods are a necessity. For instance, an organization might decide to move from a legacy SQL database to a data lake or another flexible system.
  • One source of truth
    • Another common purpose of migrating data is to move all the data into one place that is accessible by all company divisions. Sometimes this occurs after acquisition when systems need to be combined. Or, it can happen when various systems are siloed throughout a company. For instance, the IT department may utilize one database while the Marketing group uses another database, and these systems can’t “talk” to each other. When you have different databases that are contradictory, it’s hard to get insights from your data.

Challenges in Database Migration

Since database migration is a very complicated task, it is clear to face various difficulties. Here are some of the common challenges:

  • Data Analysis at the earliest 
    • At times, information can be hidden in obscure places because of constraints in computer systems. This happens because there aren’t explicit fields to hold all data components, or users may not know about the available fields’ purpose.
    • This way, the data transferred during the migration will be inadequate, inaccurate, and outdated, often found after the project has been finished. The outcome won’t have sufficient opportunity or the right resources to recognize and address this data. It is recommended to perform thorough data analysis at the soonest possible occasion, ordinarily when planning and designing the data migration service. It can help you in divulging these hidden errors.
  • Identification of Databases stored at different places
    • Over time, every organization assembles some amount of data. If your organization has been operational for a while, there is a possibility that the data is housed in different databases across various levels within the organization. The biggest challenge in migrating the databases recognizes the location of the databases in your environment. Likewise, after identification, it is challenging to decide how to normalize and convert the schema.
  • Lack of Integrated Process
    • The general process of data migration involves different people utilizing various technologies. For example, using spreadsheets to document data, which are typically prone to human errors, is hard to interpret while doing data analysis or performing data transformations.
    • The utilization of various technologies can sometimes lead to data transfer failure and its design between the analysis, development, testing, and actual implementation phases. In some cases, things get lost in translation, which eventually increases the cost and wastes time. For this, organizations must utilize the platform at its full potential. It should have the option to successfully link the critical inputs and outputs from each stage to diminish the mistake and save time and money.

How to Do Database Migrations

DB migration is a multi-step process that begins with assessing the source system and completing testing the migration design, and reproducing it to the product build. However, a data migration tools are utilized to make the database migration service and database managed service more useful.

Here are the different database migration steps:

  • Understanding the Source Database
    • You have to initially understand the source data that will populate your target database before beginning any database migration project.
    • The following are a couple of things you should know:
      • What is the exact size of the source database? The size and intricacy of the database you are trying to migrate will decide your migration project’s scope. This will likewise determine the amount of time and computing resources needed to transfer the data.
      • Does the database contain ‘large’ tables?’ If your source database contains tables with millions of rows, you should utilize a tool with the capability to load data in parallel.
      • What sort of data types will be involved? Suppose you migrate data between different database engines, such as an SQL database to an Oracle one. In that case, you will require schema conversion capabilities to execute your data migration project effectively.
  • Assessing the Data
    • This step involves a more granular evaluation of the data you want to migrate. You would need to profile your source data and characterize data quality rules to eliminate inconsistencies, duplicate values, or wrong information. Data profiling at the beginning of migration will help you mitigate the risk of delays, budget overruns, and even complete failures. You will likewise have the option to define data quality rules to validate your data and improve its quality and accuracy.
  • Converting Database Schema
    • Heterogeneous database migrations that include migrating data between different database engines are moderately more complex. While heterogeneous database migrations schema can be changed manually, it is frequently very resource-intensive and time-consuming. Therefore, utilizing a data migration tool with database schema conversion capability can help speed up the process and move data to the new database.
  • Data migration
    • After you have finished all the preliminary requirements, you’ll actually need to move the data. This may involve scripting, utilizing an ETL tool, or some other tool to move the data. During the migration, you will probably transform the data, standardize data types, and check for blunders.
  • Testing and tuning
    • When you’ve moved the data, you need to confirm that the data: was moved correctly, is finished, isn’t missing values, doesn’t contain invalid values, and is valid.


As new platforms keep hitting the market and businesses move faster every day, data migration will become a near-constant process in IT. Once you finish your first AWS data migration service, your team can run a full audit of the process to understand strengths, weaknesses, and blunders well. It would be best to document everything in your project management software and set up a clear, repeatable plan for the future.

While performing a database migration, you can also achieve:

  • Parallel processing engine and high-availability feature that guarantees you optimal performance with minimal downtime
  • Data synchronization capability that causes you captures changed data and saves time and processing power spent on bulk data loads.
  • Advanced data profiling and quality features that permit you to validate data against custom business rules to limit mistakes and inconsistencies
  • Support for a range of cloud-based and on-premise databases to cater to any data migration use-case
  • Intuitive planning to perform complex data transformations in a code-free manner

Picking a deployment model that aligns with business requirements is vital to ensure that any data migration is smooth and effective and delivers business value in terms of security, performance, and ROI.

Tags: , ,

About the Author

Varsha Solanki is a Digital Marketing Strategist at Globalvox Inc, a Cloud Transition Services. She has 3 years of experience in the Information Technology industry. She spends her time reading about new trends in Digital Marketing and the latest technologies.

Leave a Reply

Your email address will not be published. Required fields are marked *

Back to Top ↑