Today, every business gathers a sizable quantity of data. From app usage statistics to repeat purchases stats, from time spent on the app to the location of buyers, almost every data point is collected and stored in databases. We all know the importance of data in the growth of a business. From stocking inventory in anticipation of demand to focusing on more profitable customers and areas, and optimizing employee productivity, data gives us accuracy.

In today’s cloud ecosystem the demands for high functioning and high performance observability, security and networking functionality for applications and their network traffic are as high as ever. Historically a great deal of this kind of functionality has been implemented in userspace, but the ability to program these kinds of things directly into the operating system can be very beneficial to performance.

Hello everyone, Hope you are all doing well. We are aware that API testing is getting a lot of attention nowadays and we testers are more curious to explore it. But, often we felt lost in the middle of nowhere. It’s because of the lack of the basics. Before handling the API, you should know some basic stuff about that API. What type of API is that? What protocol is used by the API? Sounds interesting? Today we will learn about the types of APIs and the types of protocols used by the APIs.

A Data Lake is used to refer to a massive amount of data stored in a structured, unstructured, semi-structured, or raw form. The purpose is just to consolidate data into one destination and make it usable for data science and analytics algorithms. This data is used for observational, computational, and scientific purposes. The database has made it easier for AI models to gather data from various resources and implement a flawless system that can make informed decisions.

All software must be tested to ensure that it works as intended before being released to the public. Testing must be rigorous to ensure that users are satisfied with it and have no complaints. A bad review, or negative word of mouth, of your software, can hamper its success on the open market or mean that staff will not use the new software.

After working in UX for many years across several companies, I can confidently say this: usability testing is always a fragile issue. Companies expect their product to be exceptional and deliver value to their customers but believe that user experience testing is a time/cost sink. I’ve heard the claim (countless times) that user experience testing limits and prevents dev teams from pushing new products and features.

Every developer has experienced the pain of debugging. We know that you know what we’re talking about. Who hasn’t been lost jumping between different windows troubleshooting their code, log-jammed trying to track bugs in a pile of logs, or been caught screaming at a rubber duck that’s blankly staring back at them? But it shouldn’t be that way.

The telecommunications industry continues to develop hybrid data architectures to support data workload virtualization and cloud migration. However, while the promise of the cloud remains essential—not just for data workloads but also for network virtualisation and B2B offerings—the sheer volume and scale of data in the industry require careful management of the “journey to the cloud.”

In this post I will demonstrate how Kafka Connect is integrated in the Cloudera Data Platform (CDP), allowing users to manage and monitor their connectors in Streams Messaging Manager while also touching on security features such as role-based access control and sensitive information handling. If you are a developer moving data in or out of Kafka, an administrator, or a security expert this post is for you. But before I introduce the nitty-gritty first let’s start with the basics.