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.

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.

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.

Understanding embedded analytics dashboards starts with knowing what the term itself means—so let’s break it down. Dashboards are screens or pages that display information in a unified view that makes data easily digestible for end users.

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.

More often in today’s society of growing ‘entrepreneurs’, almost all are developing Minimum Viable Products MVPs that breathe life into their concepts. A shocking but true statistic reveals that 90% of startups fail. One big reason for this is the poor execution of a key tool in the startup world: the minimum viable product (MVP). One of the reasons for this failure is the lack of tests conducted appropriately.

Fivetran Managed Data Lake Service brings automation, governance and reliability to data lakes.

In the world of data management, clearinghouses often serve as intermediaries, enabling secure data transactions across different platforms and ensuring compliance with industry regulations. However, as data management needs evolve, businesses are increasingly looking for alternatives to clearinghouses that offer more flexibility, control, and advanced features.

Testing in production involves evaluating software directly in a live environment as real users. It’s becoming increasingly common.

Imagine your app faces a sudden surge in users. Thousands log in at once. Can your system handle the pressure, or will it crash?