Systems | Development | Analytics | API | Testing

Mobile app testing can be time-consuming, error-prone, and sometimes expensive if not done the right way. But mobile app testing is a critical part of the development process, so skipping it comes at a huge cost to end UX. To compete with the millions of mobile apps on the market, your set of best practices must ensure that the user experience is incredible for every consumer every time they use your app, starting from the first try. Not sure where to begin?

While traditional ETL (Extract, Transform, Load) collects data within a centralized data warehouse, reverse ETL flips the target and destination of the standard ETL process. This allows information to be pushed out of data warehouses and into powerful third-party operational systems that can provide better analytics and reporting services. With the Xplenty platform and its ETL tools, all information is sent between warehouses and third-party operational systems in an efficient and secure manner.

If you're looking for a better way to organize your data and ensure it stays up-to-date, you need to start utilizing CDC processes today. Change data capture uses various techniques to detect changes made in source tables and databases in real-time. Read on to learn more about change data capture and how it can be implemented to better serve your business.

There are many ways that Apache Kafka has been deployed in the field. In our Kafka Summit 2021 presentation, we took a brief overview of many different configurations that have been observed to date. In this blog series, we will discuss each of these deployments and the deployment choices made along with how they impact reliability.

Every business, large or small, needs applications. Naturally, to keep up with this need, application designers and developers need to stay on their toes with app creation. But building applications is no piece of cake; developers need to stay abreast with the latest designing trends, tech know-how, and hands-on app testing experience, not to mention, possess incredible working speed.

With the explosion of the machine learning tooling space, the barrier to entry has never been lower for companies looking to invest in AI initiatives. But enterprise AI in production is still immature. How are companies getting to production and scaling up with machine learning in 2021? Implementing data science at scale used to be an endeavor reserved for the tech giants with their armies of developers and deep pockets.