Companies are under pressure to deliver faster innovation, enabled by cloud-based data analytics and AI. In order to deliver faster business value, data teams are looking to achieve speed and scale through data and AI pipeline performance and efficiency. A recent MIT Technology Review Insights report finds that 72% of technology leaders agree that data challenges are the most likely factor to jeopardize AI/ML goals.

Adobe launched Adobe Experience Platform Federated Audience Composition, now generally available on Snowflake, allowing organizations to unlock seamless interoperability for marketers by integrating Snowflake's AI Data Cloud with Adobe Real-Time Customer Data Platform (CDP) and Adobe Journey Optimizer.

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.

Picture this: It's Black Friday, and you're circling a packed mall parking lot. Every space is taken, and cars are lined up waiting for spots. You keep circling, but there’s just no place to park and you run out of gas. When you see a java.lang.OutOfMemoryError it’s just like what you experienced in that overcrowded parking lot. The Java Virtual Machine (JVM) has run out of space to "park" new objects in memory. Now here's the thing about Java: it loves objects. It can't get enough of them.

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.

To enhance security and ease operational burden, many organizations with data lakes or lakehouses want flexibility to securely integrate their tools of choice on a single copy of data. An open standard for storage format and catalog API has helped, but there’s still a need for open standards for the catalog, including a consistent way to apply security access controls to data.

APIs are essential to modern applications, but managing access and security policies can be complex. Traditional access control mechanisms can fall short when flexible, scalable, and fine-grained control over who can access specific resources is needed. This is where OPA (Open Policy Agent) steps in. OPA provides a unified framework for consistently defining and enforcing policies across microservices, APIs, Kubernetes clusters, and beyond. Consistent policy management is essential for enterprises.

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.

Let’s Take an e-commerce platform, where there are different services for user authentication, product catalog, and order processing. These services communicate through APIs. For example, the order processing service needs to get product details from the catalogue service. Contract testing ensures that the agreement between these services—specifying what data the order service expects from the product catalogue service—stays consistent.