Cybersecurity is one of the top concerns for businesses today. Vulnerabilities make the news when they turn into expensive breaches. With the cloud so prevalent in today's digital landscape, much attention has turned to API security. Network attacks aimed at APIs increased by an incredible 681% in 2022. Considering the continued popularity of SOAP (simple object access protocol) APIs, it is now a top priority for global IT leaders to learn SOAP's security best practices.

When it comes to the quality of software solutions, it is often said that the best testing goes a long way. Just think about the day when you finally launched the perfect software with error-free elaborate systems that have surpassed users’ expectations and, on top of that, provide it in the sea of competitive applications—to have that is to welcome an outstanding testing partner.

What are some common AI/ML use cases? What use cases should business and IT leaders track as they shape a holistic process automation strategy? Let’s find out. Artificial intelligence (AI) refers to computer systems that mimic human thought and decision-making. Within AI, deep learning and machine learning play crucial roles. Deep learning uses neural networks to process large amounts of data and uncover patterns.

Since its inception in 2001, agile development has completely evolved, and today, it’s considered incomplete without agile testing.

Since the use of mobile technology is set to advance further, testing these real devices is crucial and requires effective and efficient methods. The true concept of mobile device testing guarantees that applications perform optimally on various devices. According to Statista data, the current global number of smartphone users is 4.2 billion and is expected to rise by 1.7 billion between 2024 and 2029.

Software quality has evolved over time. Initially, the focus was on finding and resolving defects prior to a product launch. As technology advanced, the emphasis shifted towards maintaining quality across the whole development process, particularly with the rise of Agile and DevOps methodologies. Quality is now approached proactively, encompassing all phases of the product lifecycle - from planning to deployment.

In software testing, ensuring that all components of a system function correctly when integrated is critical for delivering reliable applications.

As software systems become more complex, integration testing becomes even more important for their reliability and performance.

In the fast-paced world of data, where volume, variety, and velocity are constantly pushing boundaries, organizations are facing unprecedented challenges in integrating and harnessing data at scale effectively. Gartner just published the 2024 Magic QuadrantTM for Data Integration Tools, which recognized Confluent as a Challenger. Previously, Confluent was positioned as a Niche player in the 2023 Magic Quadrant for Data Integration Tools.