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.

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.

Importance of Data in Test Automation

Software development comprises of phases like gathering requirements, building, and testing the software followed by installation. In the development phase, the software may be exposed to all possible bugs and defects owing to human errors or technical glitches. Testing helps in detecting those errors and execute testing without any technical issues to ensure an outstanding customer experience.

A thorough software testing means checking all the components and elements of applications. The testing phase also includes all possible test case scenarios to extract accurate information and design the test cases considering the requirements. There can be a severe impact on the overall test process based on the type of data we select for test cases.

What is Test Data, and why is it necessary for testing?

To prepare test cases for software testing it is essential to select the right set of data. Test data is the data which are used to test the quality of the software systems. There are many ways to get the most appropriate test data for application testing. A skilled tester will be capable to source or produce test data to execute software testing to validate software performance and compare the desired results with the outcome.

Likewise, when we need the right set of data to evaluate quality of the software, we may also need data to test negative scenarios. Carefully choosing data will help in avoiding unexpected, unusual, and extreme results. If users enter the wrong information, the test case scenarios, which have been prepared out of most relevant data will continue to provide the most apt search result.

The production test data works best with a proper simulation of the real system during the testing phase. Ideally, both production data and synthetic data are used for testing, but in certain cases, production data is masked before using it for testing.

Why data is the key to test automation?

  • Test Data serves as the input, which is mandatory to test the application
  • Feeding the test data helps in validating the output if it is correct
  • Test Data selection helps in verifying different scenarios in testing
  • The test results are based on the inputs we feed in the system
  • Test Data is useful for the users to define the outcome
  • Test Data enable developers to track issues during the fix
  • Test Data confirms that applications provide expected result based on the input
  • Test Data allows you to be focused on systematic ways of feeding the data to the system
  • Test Data aids the tester to record the test case for future reference and data re-use

Types of test data and their importance

  • Valid Test Data:

Valid Test Data are supported by the application and helps in verifying the system functions and receiving an expected output whenever input is provided.

  • Invalid Test Data:

Invalid Test Data ensures that the application is working correctly. Including unsupported data format when these invalid values are provided for testing the application shows an error message to notify that the data is improper to function.

  • Boundary Test Data:

Combining the boundary value of an application, this type of data helps in removing defects that are connected while processing boundary values. However, these data are enough to handle applications. If the testers violate the process, then it may break the application.

  • Blank or Absent Data:

The files that do not contain any data and help in verifying the application response when no data is entered into the software is called blank or absent data.

Different ways that the test data are prepared:

  • Manually creating test data:

This method of test data generation is the simplest. It includes valid, invalid, null, standard production data, and data set for performance. It takes a longer time to generate test data manually with less productivity. A skilled testing team can create this test data without any additional resources. Lack of strong and in-depth technical knowledge, the testers would generate data with errors.

  • Using back-end data:

Back-end data generation helps in using back-end servers with a database and procure the data quickly. In this method, the need for having front-end data entry is removed. There is no need for technical expertise to create backdated entries. However, if the technology is not implemented, it might have an impact on the database and application.

  • Automated Test Data Generation:

Automated test data generation is most suitable to achieve excellent results with a high volume of data. The benefits of this kind of report generation are that a high volume of data is obtained at high-level speed and accuracy. The speed of delivering output is fast without any human intervention. 

  • Using third-party tools:

Using third-party tools makes it easy to create and extract data in the system. These tools have complete knowledge of the back-end applications and are built with a purpose to procure the data real-time with high accuracy. The test cases are built for future references that enable to execute test cases based on previous test case scenarios with accuracy and speed.

Challenges of test data sourcing

  • Accessing tools for achieving data sources can be an issue if the testing team does not provide permission to access the link. 
  • The lack of adequate and right tools can pose a challenge to source the test data.
  • The defects or bugs need to be identified at the earliest to run the testing for the software.
  • The testing phase may run for a longer time as most of the data are created during the execution phase.
  • Not all testers are skilled enough and have in-depth knowledge to create alternate test data solution for test data management.

Ways to control test data

Test data determines whether it is working as per expectations. Following are the ways to control test data: –

  1. Discover and understand nature of test data
  2. From multiple data sources extract a subset of production data
  3. Mask sensitive test data
  4. Compare the expected with actual result and automate the results
  5. Finally, refresh the test data

Conclusion:

Yethi’s team consists of experienced quality assurance professionals exclusively focused on the BFSI industry. We have in-depth knowledge of the domain and technology systems supporting it. We help many marquee organizations to achieve their business objectives within the stipulated timeframe, without compromising on quality.

Yethi’s flagship 5th generation codeless test automation tool – Tenjin is an intuitive automated test suite. We have a test repository of 450k+ test cases, which help us in providing cross-platform testing across multiple devices and networks with high test coverage throughout all aspects of digital transformation and application testing like functional, UI/UX, performance, compatibility, security, geo-based and network testing. This smart and intuitive tool can switch between API’s and the GUI, making it a perfect testing tool for regression testing in open banking applications.

Discover the Test Data Management Techniques in Banking that empower Software Testing

The Growing Need for Test Data Management

Early in 2018 four major Banks in the US had clients up in arms when, for several hours, banking services were not accessible via their mobile banking apps. The interrupted services were attributed to a major spike in traffic due to it being payday. Notwithstanding, reputational damage had been done to the banks and, adding to their woes, a social media tirade on platforms like Twitter, where irate clients vented their frustrations and further lambasted the banks, ensued. The experience of these banks again highlighted the importance of software system testing in banking and financial services. However, software system testing is only as good as its test data management strategy, which should be adopted and implemented to:

  • Manage test and development processes to meet testing and application development requirements
  • Secure data and streamline cloning processes, delivering clones needed to meet upgrade and patch cycles as well as maintain data security
  • Identify appropriate replicable accounts and transactions from production to meet test criteria
  • Mitigate the threat of identity theft concerns among consumers and regulators
  • Improve turn-around-times during system upgrades through improved planning of data refreshes and overall data utilization.

The importance of test data management in testing

At its core, data management is the science of creating and maintaining the data sets generated by software system testing, driven by cause-effect relationships, producing predictable outcomes and responses. These data sets can be generated entirely by the tester, by simulating each stage of the customer or transaction journey, producing what is called synthetic data. Alternatively, data generated by actual transactions and previously recorded is called migrated data, providing authentic sets. Each of these forms of data generation has its value and challenges.

Synthetic data needs to be interoperable, able to account for diverse systems in varying environments without diluting the complexity of inter-relationships. Besides, the data needs to be refreshed and valid for multiple rounds of testing and if in an automated environment, should not need to be updated by manual intervention. Migrated data, in turn, is drawn from authentic transactions has limited reusability across different test-cycles, hamstrung further by data confidentiality obligations.

Mitigating the risks inherent in Test Data Management

Implementing a Test Data Management strategy requires that several risks and their potential impacts be considered. The complexity of the data, structured or unstructured, whether the databases are new or from legacy systems and how the data is stored. If kept in multiple environments, it may have the added challenge of access sensitivities and potential confidentiality breaches. Assessing the time available for data discovery, its generation and management is another factor that is paramount in ensuring representative samples are obtained. Besides, test data management requires different types of data for different types of testing, from performance through to user acceptance testing, so data needs to be organized correctly to fit within the required time and budgetary constraints. Another potential risk is the measure to which the organization operates in a distributed multi-supplier or outsourced environment, with multiple users accessing the data in multiple locations. Operating in such an environment also highlights the importance of data security and protection. As a standard, new security protocols will be required as well as staff training, highlighting the importance of protecting live production data as well as guarding test environments.

Empowered Software Testing through Test Data Management

Given the dynamic nature of the banking environment and the need to ensure optimized and efficient process continuity, any process that does not facilitate this expediency will have a negative impact on the entire system. Accordingly, if the process of choosing, interpreting and scrutinizing test results is time-consuming, arduous, and requiring specific knowledge of underlying applications, it will have a direct negative impact on the entire system. The time it takes for reporting and information sharing in an organization can also be a significant factor in the efficiency of the entire organization.

To meet these dual needs of time and efficiency, banks should implement the following test data management techniques, while taking care to mask sensitive information:

  1. Database cloning through the copying of production data
  2. Data sub-setting by substituting production data when appropriate, &
  3. Synthetic data generation, through the production of synthetic data based on a clear understanding of the underlying data model, which requires no de-identification

Yethi, a leader in software solutions and quality assurance in global banking and financial services has developed substantial expertise in creating end-to-end enterprise-wide test data management services which include data quality assessment, data masking, data subset, data archiving, data cleansing and data optimization. Yethi also assures that through rigorous control the thorough management of test data is ensured, beginning with quality assured consistent data. Data security and privacy are also safeguarded, while storage requirements and software costs are significantly reduced.