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.”

Users continue to give Kong top rankings in the Fall 2022 G2 reports, landing Kong in the leader quadrant in the API Management Tools G2 Grid and the Top 5 Easiest to Use API Management Tools. There are plenty of reasons why Kong is king when it comes to API management. But don’t just take our word for it! See what some of our users have to say.

Smartphones have revolutionized our lives, and you’re likely reading this on one. It’s hardly shocking to consider how pervasive mobile gadgets have become. Nearly half of people polled in a February 2021 survey conducted by L.Ceci via Statista spent five to six hours a day on their phones, not considering work-related smartphone use. Mobile devices handle more than half of all searches worldwide. Additionally, page speed is becoming a factor in mobile search engine rankings.

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 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.

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.

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.

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.

RubyCritic provides visual reports highlighting code smells, code structure, ease of testing, and test coverage in your Ruby application. It's in active development, with new code analysis tools often being introduced as new features. It's well worth keeping track of RubyCritic's releases. This article will touch on some of RubyCritic's benefits, its dependencies, and how to read its code reports. Let's get going!