Systems | Development | Analytics | API | Testing

Automation and hybrid deployment complement each other to provide the utmost security in data integration.

Learn the strategies for managing AI risk and compliance in regulated industries.

Integration testing is the second step of the software development process, occurring after unit testing. Once all the different components have been designed and tested, it’s time to put them all together. This process can be completed in a number of ways, depending on your preferences as a tester. Today, we’ll break down some of the most common questions about integration testing.

How the world of generative AI has changed and what new innovations and capabilities mean for you.

How technical and business data catalogs work together to make data legible and actionable for everyone.

This expert panel discussed how businesses can prepare for challenges and opportunities with AI, powering smarter models and personalized experiences.

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.

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