Data Migration Testing: Strategy & Best Practices

Data Migration testing strategy & best practices

As the world is witnessing a huge transformation at the technological front, organizations are constantly upgrading their legacy systems to keep up with the trend. Though updating to new systems is the need of time, a major challenge lies in migrating data without losing it. Hence, it becomes important to plan out an efficient data migration strategy to ensure that migration happens without any data loss.

Testing of migration is as important as migrating data; failing to do so, organizations may face issues of discrepancies causing expected results, which can affect the organization adversely. Furthermore, to carry out efficient migration testing a well-defined strategy is required, without which the organization can be left financially drained of resources after setting up more processes than they need. They may even find that their commercial success is negatively influenced by not exploiting their data to the fullest.

What is Data Migration? Why Do Organizations Undertake Data Migration?

The process of moving data from one system to another, preferably from a legacy system to a new one, is known as data migration. However, the process is not as straightforward as it may seem because it involves a change in storage and database or application. The data migration process involves three defined steps extracting data, transforming data, and loading data. When the data is extracted from the sources, it must go through a series of cleansing to eliminate errors and inaccuracies to qualify the data for efficient analysis and load them to the targeted destinations.

Organizations perform data migration for varied reasons; first, as a part of their system revamping plan, the other possible reason could be during the upgrade in databases, while another possibility could be when creating a new data warehouse or merging data from acquisitions. But it’s most common when teams are deploying other systems alongside their existing applications for integration purposes.

Why is Data Migration Strategy Important?

A comprehensive data migration strategy comes in handy when performing large-scale operations that need to preserve business continuity simultaneously. Organizations perform data migration to improve performance and competitiveness. When organizations carefully control the data migration process, they can prevent delays caused by missing deadlines or exceeding budgets, while improperly managing the process can leave a lot of migration projects dead in their tracks. In planning and strategizing the work, teams must ensure that they put their best foot forward with undivided focus on one project.

Data Migration Strategies

There are several approaches to developing a data migration plan, however, the two major data migration strategies include “big bang” or “trickle.”

  • ·         ‘Big Bang’ Data Migration

Organizations follow big bang data migration to ensure that the data is moved from the legacy systems to the target destination and the full transfer is done in a limited time. As the data migration process goes through the three inevitable steps of extraction, transformation and loading, the active system may experience a little downtime towards transitioning to the new database. The data migration process has some challenges like validation implementation failure, lack of data analysis scope, and inability to validate specification to name a few. But companies implement this strategy as the entire process of data migration takes less time to complete even with many challenges

  • ·         ‘Tickle’ Migration

Tickle migration is conducted in phases to avoid downtime or operational interruptions. In addition, migration staging is conducted continuously to support migration, even during peak operations.

Key Components in Data Migration Strategies

Moving sensitive or important data isn’t a simple task as it involves a lot of aspects that would need consideration. Hence, it is not a good idea to begin the process without having a plan on how this should be done. One must consider the key components of data migration strategies based on the critical factors mentioned below.

  • Knowledge of data — It is critical to have adequate knowledge of the source data to find the solution to issues that may arise unexpectedly. Hence, consider doing a thorough audit of the source data before migration.
  • Data cleansing — Between source data extraction to data transformation, there is a critical step of data cleaning, which focuses on identifying the issues of source data and resolving them.  The data cleaning can be done using software tools and third-party resources. 
  • Data quality maintenance and protection — The quality of data may subside over a period. It is critical to maintain and protect the data quality to ensure the reliability of the data migration process.   
  • Data tracking and reporting — It is critical to ensure data integrity by data tracking and reporting. Use the right tool and automate the function wherever needed.  

Although we can follow many ways to move data, it is important to have adequate knowledge about the best practices to ensure that the process of data transfer is done systematically and seamlessly.

  • ·         Solid Planning

Good planning is half work done. Decide the systems that will need to be migrated and plan how they will affect the business. When migrating data from one system to another, always ask yourself if your changes can be made without affecting or hindering other systems already being used by the business. Solid planning will help in carrying out the entire process with utmost ease.

  • ·         Action Steps

It’s time to give your migration process a ticking clock and a detailed, step-by-step plan– including the plan of execution – what, who, why, and deadlines – to ensure your migration is successful and time-bound.

  • ·         Crosscheck

Decide what technology to use for the migration and how it will fit into the larger IT ecosystem. Make sure you have a plan in place for decommissioning old systems.

  • ·         High-Quality Conversion Process

Ensure you map out the technical details related to how you plan to move data. Then, put processes in place to ensure that your data stays organized and of high quality.

  • ·         Build & Test

Here, you will implement the software logic that performs the conversion of data. Test the script in a mirror sandbox environment instead of running it against your production database.

  • ·         Execute

You’ll need to verify that data migration processes are safe, reliable, and fit for use in your business before implementing them.

How to Make Your Data Migration Go Smoothly?

Transferring sensitive data is a complex yet delicate process. However, here are some best practices to follow to ensure a successful migration.

  • ·         A Thorough Migration Plan

It would help if you had a good idea about how much data to move, from where it will come, and an idea of how you’re going to implement its move into your target server or location. Your plan should outline each necessary step and who will be responsible for them, physical aspects such as technical or compatibility issues, downtime expected for your system, and the source data and migration tools if they are going to be used. Last but not least is protecting your data’s integrity. Backups may prove exceptionally helpful in preserving your original data.

  • ·         Examine your Data

Before you proceed, take a close look at the data that you’re going to be migrating. In particular, identify and weed out data that is outdated and no longer important. Separating it from your migration will help streamline your process and set a clean slate for your team after the migration is complete. If there are pieces of information that require security controls due to the nature of its regulatory information, make sure you take these details into account.

  • ·         Put Migration Policies in Place

A data migration policy ensures that your data is on the right path after it’s been migrated. It also organizes and gives control over who will handle it and how they will do it, along with adequately protecting your company’s sensitive data.

  • ·         Automatic Retention Policy

Once you’ve successfully migrated, you must take the time to ensure that everything is placed where it belongs and remains safe and secured. It’s essential to keep all your systems in working order by setting up automatic retention policies to prevent data leakage. Also, make sure that outdated data has been validated and permissions are granted accordingly. Finally, just ensure that old legacy systems will back up automatically in the event of any technical difficulties – but make sure to double-check them before they’re put on standby!


As technology continues to change, businesses must continue to evolve as well. As a result, companies must create a plan for their data and understand data migration in today’s business world. Data migration can be challenging, but a company can migrate its data with minimal downtime and stress with a proper strategy and a few best practices.

At Yethi, we have the expertise of handling complex financial data migration, with pre and post-migration testing along with regular audits. We offer the most efficient end-to-end testing service. Our test automation platform, Tenjin, can test large data migration easily and efficiently while reducing time and cost significantly.

Testing Strategy for Big Data Migration

Testing strategy for big data migration

Big data migration is way more complicated than a mere “lift-and-shift” migration. One of the major concerns is data security when migrated to the cloud. Companies adopt hybrid cloud solutions to protect sensitive data. They differentiate computing and storage data and implement role-based access to ensure data safety on the cloud.

As big data has already created a lot of buzzes recently, organizations across all major sectors are trying to leverage it for their organizational growth. But due to a lack of technical skills and knowledge of data integration practices and tools, developers cannot always fully ripe the benefits of a cloud-based big data environment while moving the on-premises data to the cloud.

Big data is a field that deals with the identification and evaluation of voluminous and complex data sets, and migrating these voluminous data requires monitoring, which increases operational costs. The code-writing process is usually time-consuming, and without automation, it has a high risk of human error. It is important to note that big data does not focus on quantity. Instead, it focuses on extracting meaningful information from these data, which the company can utilize.

When organizations upgrade their legacy systems, they undertake the most complex task of big data migration. The migration process requires a clear testing strategy and an efficient team to prevent data loss.

What is Big Data testing?

Big Data testing is a set of methodologies that ensure whether different Big Data functionalities and operations perform as expected. Enterprises perform Big Data testing to assure that the Big Data system runs smoothly, without any error/bug. The test also checks the performance and security of the system. Big Data professionals perform such testing when they have updated the software, integrated new hardware, or after data migration. Big Data migration testing is the essential phase of data migration as it checks whether all the data got migrated without loss or damage.

Big Data is an accumulation of data with a large volume of greater variety, that grows exponentially with time. Every enterprise generates a vast collection of data which is so voluminous that it becomes difficult for the conventional data processing applications to handle them. Hence, Big Data technologies, software, and methodologies are created to deal with challenges associated with big data processing. Big Data deals with the three V’s – Volume, Velocity, and Variety, which has eventually become the mainstream definition of Big Data.

Data Migration and its Challenges:

The technological evolution has led every enterprise to migrate its data to advanced systems. The prime reason for migration is the availability of the Cloud. Migrating this immense volume of data to the Cloud helps productivity improvement, cost reduction, and flexibility in data management for the organization. When such a large volume of data migrates to the Cloud, Big Data migration testing becomes a vital phase. It checks the condition and connectivity of the overall data. Data migration has to face a wide array of challenges. Some of them are:

  • Mismatched data type:

During data migration, the data type needs proper mapping. It is essential to check the variable-length fields.

  • Corrupt data or incorrect translation:

For a single Big Data storage, multiple source tables store various formats of data. It is crucial to conduct a thorough data analysis when the architecture shifts from a legacy system to a modern Cloud-based system. The verification will check whether any data is corrupt or not.

  • Data loss or data misplace:

Data migration also experiences another critical issue, which is data loss. It happens when data backup takes place or there exists some illogical analysis of data.

  • Rejected row:

When data shifts from the legacy system to the target system, some data gets discarded during data extraction. It usually happens when automatic migration of data occurs.

Strategies in Big Data Migration Testing

Big Data migration testing is an essential phase of migrating large data volumes. Various types of testing occur before and after the migration. The big data testing team has to prepare some strategies to cater to the multiple testing to understand the data validation and outcome of the test. The phases of big data testing strategy include:

  • Pre-migration Testing: There are several testing strategies and techniques that take place before the data migration.
    • The team should understand the scope of the data correctly. It includes the number of tables, record count, extraction process, etc.
    • The testing team should also have a fair idea of the data scheme for both the source and the target system.
    • The team should also validate whether they can understand the data load process or not.
    • Once the test team understands all these, they should now ensure whether the mapping of the user interface is correct or not.
    • The testing strategy should also involve ensuring & understanding all business cases and use cases.
  • Post-migration Testing:

Once the data gets migrated, the tester(s) should accomplish further tests against the subset of data.

  • Data validation and Testing: This test ensures whether the data collected to the new target system is correct and accurate. The team performs this validation by entering the collected data into the Hadoop Distributed File System (HDFS). Here a step-by-step verification takes place through different analytic tools. The schema validation should also come under this phase.
    • Process Validation: Process validation or Business logic validation is where the tester checks for nodes associated with the business logic at every node point. This process uses Map Reduce as the tool, which validates the key-value pair generation.
    • Output Validation: The last phase of the big data migration testing is where the data gets loaded into the target system. Then the Big data testing team should check whether the data has experienced any distortions. If there is no distortion in data, the testing team transfers the output files to the Enterprise Data Warehouse (EDW).

Big Data Migration Testing Tools

A variety of automation testing tools are available in the market for testing Big Data migration. The test team can integrate these tools to ensure accurate and consistent results. These tools must hold certain features like scalability, reliability, flexibility at constant change, and economical.


Due to the exponential increase in data production, organizations are shifting their data storage technique to Cloud. Hence, Cloud has become the new standard, and Big Data migration has become necessary. So, while shifting from legacy data storage techniques to the latest technological advancement, every organization should perform big data migration testing to check the data quality.

Yethi is a leading QA service provider for global banks and financial institutions. We understand the importance of complex financial data migration and make sure to offer the most efficient testing service. We have the expertise to handle complex data migration, with pre and post-migration testing along with regular audits. Our test automation platform, Tenjin, can test large data migration easily and efficiently while reducing time and money significantly.