By now, every organization, regardless of industry, has at least explored the use of AI, if not already embraced it. In today’s market, the AI imperative is firmly here, and failing to act quickly could mean getting left behind. But even as adoption soars, struggles remain, and scalability continues to be a major issue. Organizations are quick to adopt AI, but getting it established across the organization brings a unique set of challenges that come into play.

The relentless rise of ChatGPT and other Large Language Models has brought a major breakthrough in the world of artificial intelligence. With key industry players like Meta, Google, Nvidia and Microsoft competing fiercely to dominate this space, it’s likely we’ve only seen the start of the innovation. Several big companies have made their AI Models and platforms available for the public to use and build on.

Hey, you! Yeah, you! The puzzled-looking Spring Boot developer, scouring the web for a guide on integrating your microservices with Apache Kafka on Confluent Cloud with Stream Governance. Admit it, you’ve been Googling nonstop for the past hour and all you’ve found are examples using StringSerializer/StringDeserializer with not even the slightest mention of "schema registry-aware" serialization methods. And I bet the examples you found are implemented in Java.

Understanding API design is crucial for modern software development. Two popular approaches are GraphQL and REST. These technologies shape how data is exchanged and used over the internet, but they differ significantly in how they allow clients to interact with the server and retrieve data. Let's compare them in this article!

Whether you’re working in the marketing department or the operations department, you’ve probably heard a lot about APIs and API integrations. API integration is the process of connecting different software systems or applications through their respective APIs to enable them to communicate and share data seamlessly. These have become the “buzz” terms of IT departments in recent years as more enterprises embrace the trend toward digital transformation.

This summer marks five years since we announced our first fully managed connector on Confluent Cloud in 2019, the Amazon S3 Sink Connector. Since then, our connector offerings have not only expanded significantly but also enabled teams to send hundreds of petabytes of data throughput. Today, we support over 80 pre-built, fully managed connectors, custom connectors, and secure private networking.

Digitalization, as we’re talking about in this article, refers to the strategic use of digital technologies and analytics to shift customer interactions from physical locations to digital platforms like apps and online services.

At Snowflake, our product philosophy is centered on a single unified platform, and thus, a single core engine. To support all your workload needs with unmatched performance and efficiency, we continuously enhance and deliver performance improvements on this core engine. This approach allows you to benefit from enhanced performance effortlessly, without incurring additional costs or requiring migrations, upgrades or manual intervention.