Every enterprise is modernizing their business systems and applications to respond to real-time data. Within the next few years, we predict that most of an enterprise's data products will be built using a streaming fabric – a rich tapestry of real-time data, abstracted from the infrastructure it runs on. This streaming fabric spans not just one Apache Kafka cluster, but dozens, hundreds, maybe even thousands of them.

Gen AI has the potential to bring immense value for marketing use cases, from content creation to hyper-personalization to product insights, and many more. But if you’re struggling to scale and operationalize gen AI, you’re not alone. That’s where most enterprises struggle. To date, many companies are still in the excitement and exploitation phase of gen AI. Few have a number of initial pilots deployed and even fewer have simultaneous pilots and are building differentiating use cases.

Support for pipe syntax in GoogleSQL brings the elegance of piped data flow to SQL queries in BigQuery and Cloud Logging.

In the fast-paced, highly regulated world of financial services, delivering exceptional service isn’t just about speed – it’s about managing immense complexity. As financial institutions face increasing pressure to innovate, they also carry the weight of maintaining strict security and compliance standards. Test automation is more than just a tool; it empowers teams to modernize with confidence, knowing they can meet both quality expectations and regulatory demands.

Perforce has been contributing and working in open source for decades now. We understand that open source is the linchpin for technology that supports businesses today. Our approach to open source is not unique to the industry at large or a company of our size (we have around 1,700 employees and 800 are on my team), but questions of our approach to open source became much more visible when we acquired Puppet – which has a really dedicated open-source community and a long history with open source.

This year's Gartner Hype Cycle for Data Management report mentions self-service data management. It’s a game-changer that gives business users the power to work with data without constantly relying on IT, boosting data quality and making data available for analytics and decision-making. But what is it, really? How do you achieve self-service? Let’s take a closer look.

What’s one thing all your favorite legal shows have in common? Whether it’s Suits or The Lincoln Lawyer, they rarely show the amount of paperwork lawyers have to handle on a daily basis. Understandably so, paperwork isn’t the most glamorous part of the job but that doesn’t mean it’s not crucial. In fact, lawyers deal with tens, if not hundreds, of documents on a daily basis during most parts of their job, such as discovery, research, or drafting.

In the fast-paced world of software development, the efficiency of build processes plays a crucial role in maintaining productivity and code quality. At ThoughtSpot, while Gradle has been effective, the growing complexity of our projects demanded a more sophisticated approach to understanding and optimizing our builds. This requirement prompted us to explore Build Analytics—harnessing data from our build processes to gain actionable insights.

In this article we’ll learn about Sheets in SwiftUI. We will explain when, and how, to use them, we’ll explain how they’re created and shown. We’ll also look at a few examples of their usage. Let’s get started!

There is a sense of growing maturity in the DevOps market that is also reflected by its evolving tools. For the past some time, I’ve also seen these tools expanding beyond the limited utilities of continuous integration and continuous delivery. They are now seeping into database management, QA automation, and even AI integration in the SDLC. However, I see no deviation in their efficiency that enables teams to transition smoothly from ITOps to DevOps.