If the business owner wants to see a weekly report on sales, this would require an aggregate of the transactions from both systems. Transactions are recorded and stored differently in each type of POS system on different databases. Say there are several older stores on an outdated point-of-sale (POS) system while the newer stores are running a more modern system. Let’s use a retail business with multiple stores as a quick example. Transformation is perhaps the most critical point in the data engineering process. This is where business analysts and financial analysts have the opportunity to use the data sets to create reports, charts, and other requested metrics that inform business decisions. Once the data is extracted from the sources, it can be transformed according to the needs of the business and loaded into business analysis tools. In data engineering, we call the process of getting that data and making it useful extract, transform, load or ETL. Raw data in a source isn’t much use to anyone, which is where data engineering comes in. Whether that tool is a spreadsheet or a database is up to the business, but that original place where data is created is where we start. Data is some kind of aggregated information kept in a business tool. To understand where data testing begins, we’ll need to know how data is engineered and what makes it different from other kinds of programming, such as software development. Then we can look into data quality, and how to measure it. To understand how data testing works, we have to understand what data engineering is first. It’s understandable, as data science is a brand new field, and even those of us who work with data daily have to remain open to anything and everything changing about the way we handle our work. I have friends in technology and in software development who don’t quite understand what data testing is, why it’s necessary, or where it fits into the world of programming. “Well, I do data testing,” I try to explain, often to no avail. They don’t really understand what I mean. Tthat is exactly why I decided to continue as a QA Engineer.When someone asks me what I do for a living, I say that I am a data quality assurance (QA) engineer. Our QA process continues to improve every day and we definitely have a great mindset to continue learning. Everybody is so encouraging and supportive in your effort to learn and to develop into a better QA. From the Development team, to the PMs, to the QA team, to the leads. Eko gave me a team that I had always imagined to be a part of. I feel the autonomy that I have to achieve my objectives. Eko has not only encouraged me to learn crucial elements that I want to develop further for my role, but it has also made me love my job as a QA Engineer. I have to admit it’s the best career choice I have made so far. And of course, the QA has to have a basic understanding of all different teams and what they do (For example: Backend, Devops, etc.) so that he/she avoids sending the bug report to the wrong person, costing time to the entire product life cycle.Īfter having a bumpy ride, I found Eko and the first time I saw the product, I just wanted to work for this organization. So now you have to think of new testing techniques, think of new and unique use cases and possibly new tools (Automation, Test Management, etc.). Whether it be a new QA tool or a new product that your company has decided to launch which has no similarities with the old product whatsoever.
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