The US-based leading market researcher decided to update, digitize, and migrate their processes, systems, and databases all at once, and we knew that this would challenge us as a quality engineering services company. At the same time that we were supplying NLP (natural language processing) solutions to their writing team, we also needed to validate data faster during their cloud migration. While the company constantly continued to generate files and data, we were simultaneously migrating older files and data to the cloud. Because of this, the client needed a system that would speed up the whole process, so the migration could eventually be completed. The design needed to be powerful, fast, and economical, and to do that, we needed to provide our most robust quality engineering services.
Our client wanted to migrate their data to a new database, and they wanted to do it as quickly and accurately as possible. Our quality engineering consultants introduced the idea of using FitNesse because it validates data samples rather than validating each piece of data, ensuring a swift, accurate migration. We used FitNesse to build a generic framework to easily pull files and datasets from all sorts of places without needing to convert any files.
Built a generic framework to validate data from several sources
Free software that the client already used was utilized
FitNesse was applied across the project, allowing multiple comparisons to be run at once
To truly imbed quality engineering for this client, we began by learning what they wanted from the cloud migration. The client wanted to bring the same innovation and efficiency they have across their business to this migration, so we introduced them to FitNesse. This free Quality Engineering (QE) software enabled the client to migrate with accuracy and speed, all while keeping costs low.
The core of this project was to help our client migrate their data from one database to another. Our cloud solutions included building a regression test suite to work in conjunction with FitNesse. With this tool, our client would select sample data or files. Then the suite could identify the differences between data from the legacy tables and data from the new table. It could also compare a file with a table or another file. The data was validated, and if it failed the process, that failure was logged. Engineers would address that along with other similar files. If the sample data worked in the validation and migration, the client knew that similar data was also working. Engineers didn’t have to pay attention to successful files, allowing the process to go much faster.
Our client wanted to migrate their data quickly, and we tried to keep a firm eye on their budget. As an experienced QE testing company, we understand how to put the budget at the heart of our software quality engineering. We often turn to freeware for our projects because we believe in finding the best tool for the job rather than the fanciest one. FitNesse fits the brief perfectly as an open-source tool that speeds up data validation. We also utilized tools the client already had in their tech stack, including Oracle and Redshift. In that way, we provided tools that helped our client reach their goals for this project without blowing the budget.
With cloud migration support, quality is often sacrificed in pursuit of speed. We found a way to balance them by building quality control into the solution. Our automation tool includes an audit trail to enable employees to check past details while data is still being validated. We also ensured data validation could be viewed as an Excel report or on the FitNesse wiki pages so that many stakeholders could access results simultaneously in a way that made sense to them. Notably, we included a concise log of failed records. It allowed us to validate records quickly while correcting any issues as they occurred. Ultimately, it is how we ensure speedy validations with thorough quality control.
Our quality engineering team helped our client validate and migrate their data seamlessly. We saved time with sample data validation and the ability to input multiple comparisons at once to avoid the need to manually input data for each comparison. By using freeware and automation, we also ensured our solution was cost-effective. We enabled the client’s cloud solutions to truly meet their business goals.
Across Oracle and Redshift databases through our generic framework
Ensuring the entire process was completed on time
By testing sample data and files instead of whole sets
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