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Summary Learn about the many challenges faces by the testing team in production data testing. Learn how to be well aware and take the refight steps to handle these challenges. Body It is a great challenge to find good test data for Software testing training Toronto. After all, it is impossible for the testers to create sufficient diverse test data manually, especially for any large-scale system. One can of course take advantage of data-generation tools and create unlimited amounts of data. However, the data thus produced carries very little diversity and offer the same distribution of values. This is the reason why many clients prefer getting production data for testing from large systems where records have gathered over years of use and with different revisions of these systems. Apart from collecting the right kind of production data for testing, another challenge is to ensure privacy. The data often contains personal information about individuals and these needs to be handled carefully and securely. However, preventing the data from falling into unreliable hands and secure data handling during testing, often imposes constraints and inefficiencies. Many organizations prefer to scramble the production data before using it for testing. QA Courses Ontario make note of these restraints and issues and train their students accordingly. This anonymization process leads further to another set of challenges. It must occur under a secure environment and as it is not reversible, it should not fall in wrong hands. On the other hand the process must maintain the utility of the data for functional testing, and retain its meaningfulness. Thus, preserving the meaning of the data is another challenge. The data should be able to create queries and reflect the results accurately. Failure to apply the meaningfulness can result in major problems and influence on the value of testing. If the anonymization process scrambles the data and it breaks the integrity, then the usefulness of the data simply dissolves away. It will become impossible to test the functionality, throughput, performance reliability, and security. One must bring together a team that can determine the right tools and identify risks and constraints for the project. Options may include both commercial and customized tools. The selection of these tools is just like any other test tool and testing procedure. An organization contemplating on a project such a project should go for careful planning and execution, Taking a casual approach can only result in failure to overcome many of the above challenges. A lot of time and effort is required to carry out the production data testing and anonymization process. In an organization, the costs involved due to a particular project, are absorbed into their budgets, thus raising the costs. Moreover, the data cannot alter during extraction of anonymized data and nothing can be more complex and challenging than this. Keep in mind the above thoughts before getting into production data tasting and make sure you have the right skills and data warehouse testing. About Author Jeff Evans has penned down many articles on Data Warehouse Testing and is a well followed writer. Here, he talks about the challenges involved in production data testing.
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