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.

Conclusion

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.