I shouldn’t have to convince anyone that Apache Kafka is an incredibly useful and powerful technology. As a distributed event streaming platform, it’s adept at storing your event data and serving it up for downstream consuming applications to make sense of that information––in real time or as close to real time as your use case permits. The real beauty of Kafka as a technology is that it can do it with very little effort on your part. In effect, it’s a black box.

According to Gartner, 87% of organizations are classified as having low business intelligence and analytics maturity, meaning they struggle to extract value from their data. This alarming statistic highlights a common struggle—turning raw data into actionable insights. Product teams often find themselves overwhelmed by the sheer volume of information they collect. Extracting meaningful patterns, deciphering user behavior, and predicting market trends from this sea of customer data can seem daunting.

When it comes to managing AI projects, the Command Line Interface (CLI) can be a powerful tool. With ClearML, the CLI becomes an essential resource for creating job templates, launching remote for JupyterLab, VS Code, or SSH development environments, and executing code on a remote machine that can better meet resource needs. Specifically designed for AI workloads, ClearML’s CLI offers seamless control and efficiency, empowering users to maximize their AI efforts.

Learn about some of the top mobile testing tools and frameworks available, including how you can leverage them to improve your app quality and deliver exceptional customer experiences.

Cloud operations were once the logical next step for streamlining business processes, but now they have become the go-to solution for maximizing operational efficiency. With the advent of ‘cloud-first’ architectures and strategies, cloud computing is the rule, not the exception.

In today’s rapidly evolving tech landscape, it's tempting for businesses to chase after the latest trends—Artificial Intelligence (AI) being the crown jewel of them all. However, unless you're an AI and data analytics provider, focusing solely on AI might be a misstep.

Since generative AI exploded onto the global market, organizations have flocked to adopt it. SAP is no exception–late last year, the ERP launched its embedded AI copilot, Joule. In addition, SAP has invested in other AI companies, hired a chief artificial intelligence officer, and added generative AI features to its products. In order to spur cloud adoption, many of SAP’s premium AI features will only be available to RISE with SAP and GROW with SAP customers.