Much like Apple people tend to be all Apple, all the time, Microsoft Dynamics ERP users tend to prefer Microsoft products for all their computing needs. It’s not hard to understand why. Using products from the same ecosystem prevents compatibility issues and saves time in learning multiple systems.

Many years have passed since the first testing methodology made its way into SDLC processes. At the time, the software testing activities were simple and straightforward: validating the application against user requirements after the development stage was over. The times moved ahead, and the procedures evolved. Today, several testing types exist under the umbrella term of ‘software testing.’ Two of them are scripted testing and exploratory testing.

Imagine you’re driving a car with the engine roaring, the wheels spinning, and the wind blowing through your hair. Everything might seem perfect until you step on the brakes, and it takes 10 full seconds to bring the car to a stop. Conducting thorough testing can help detect such flaws. Non-functional testing, here, will help test the braking system’s response time, how well it works, how effective it is, its performance, and so on.

A data extraction tool can help improve the accuracy of data by automating the extraction process and reducing the risk of human error. This can lead to more reliable and consistent data that can be used to make better business decisions. Moreover, data extraction tools can help you increase productivity and improve the quality of your data as they automate the process of retrieving data from multiple sources.

Good data is the foundation of any successful business. Forecasting, planning, or tracking a company’s long-term performance is difficult without good data. Real-time information provided by data supports decision-making and helps save time and money. So, it becomes essential to test the data better to draw accurate insights.

Data engineers who work with huge amounts of data know that “big data” is not just an overhyped term. When the volumes of data get into petabytes the best data engineering tools start to break down. This is when you need devoted big data technologies that are fault-tolerant, scalable, and offer high performance even when amounts of data test the limits of your data platform. This article won’t be just another listicle. Instead, we’ll showcase the best big data tools by use case.

In the article “From Data to Insights: An Introduction to Product Analytics”, we walk you through the basics of product analytics, providing you with a high-level approach to get started with it. This time, we’d like to dig in deeper to dissect every step of the product analytics process, assuming that the main goal is to improve user engagement and retention.

How many times have you heard the sentence that starts with “This software is compatible with Windows 8 and above” or “Only compatible with Android version 6.0 and above”? I am sure this would ring a bell for all gamers as games are highly sensitive to the platform they are run on. But for non-gamers too this would be the first thing they need to check if they know they are operating on an older version of the operating system.

Imagine you’re planning a road trip across the country. You’ve got your car, a map, and plenty of snacks for the journey. But there’s one crucial thing missing – a functional engine that will get you from point A to point B without any issues. That’s exactly why API functional testing is so important. APIs are the engines that power our software applications. They allow different systems to communicate with each other and transfer data seamlessly.

Today, all applications run on API. It connects one microservice to another, clients with servers, and runs the most basic yet essential application services. Clearly, a well-oiled API is imperative to ensure the smooth functioning of every system. If API breaks down, the complete user experience and application are put at risk. And only proper API testing can secure the system from such downtime possibilities.