Systems | Development | Analytics | API | Testing

%term

Overcome ESG Data Challenges with Complimentary Gartner Market Guide

Managing ESG data is complex but essential. Gartner’s Market Guide for ESG Reporting and Management Software states, "Without integrated ESG reporting, companies risk compliance breaches and reputational harm." This guide provides a roadmap to tackle ESG challenges and build a robust reporting strategy.

The ultimate guide to Python logging

When an application runs, it performs a tremendous number of tasks, with many happening behind the scenes. Even a simple to-do application has more than you'd expect. The app will at a bare minimum have tons of tasks like user logins, creating to-dos, updating to-dos, deleting to-dos, and duplicating to-dos. These tasks the application performs can result in success or potentially result in some errors.

The Impact of AI on Software Engineering Productivity

It is hard to imagine a time not long ago where AI has not been front and centre of our everyday news, let alone in the software engineering world? The advent of LLMs coupled with the existing compute power catapulted the use of AI in our everyday lives and in particular so in the life of a software engineer. This article breaks down some of the use cases of AI in software engineering and suggests a path to investigate the key question: Did we actually become more productive?

How Australian Open handles realtime data delivery at scale

The Australian Open (AO) is one of the world’s premier annual sporting events. According to Tennis Australia, the 2024 edition - held over three weeks - reached over 558 million unique global viewers, a 57% increase from the previous year, and accumulated more than 2.17 billion cumulative viewers, up 24%. The event also attracted a record-breaking 1,110,657 fans to Melbourne Park.

Delivering Oracle quality at scale with the pod model

The pod model is an innovative approach that many enterprises, both small and large, are adopting to enhance their software development and quality assurance processes. This model involves creating small, cross-functional teams known as pods. Similar to an Agile or scrum team, each pod typically consists of five to ten members, including developers, testers, and a quality engineer (QE) or quality assurance engineer (QAE).

AI in performance testing: beyond traditional load testing

Before diving in, it's important to define three key concepts: However, traditional methods often have limitations. Fixed scenarios can miss unpredictable behaviors and identifying specific bottlenecks can be challenging without real-time insights. This is where AI comes in. In the following sections, you’ll understand how Artificial Intelligence (AI) might enhance performance testing, from creating adaptive test scenarios to optimizing resources.