Since Snowflake’s Dynamic Tables went into preview, we have worked with hundreds of customers to understand the challenges they faced producing high-quality data quickly and at scale. The No. 1 pain point: Data pipelines are becoming increasingly complex. This rising complexity is a result of myriad factors.

Unlocking the power of AI within SAP for your team requires overcoming a significant hurdle: data accessibility. SAP data’s complexity, spread across various modules, creates silos of information that your team might struggle to understand and utilize effectively. Inaccessible or misaligned SAP data will hinder your AI system’s ability to learn and deliver valuable results specific to your organization.

When you’re ready to make the transition from manual testing to test automation, it’s natural to consider hiring. A QA engineer — who has the technical skills to write and maintain automated tests in an open-source framework — can take the burden of end-to-end test management off of your development team, allowing them to ship more code, faster. But hiring a good QA engineer is — often prohibitively — expensive.

"The whole is more than the sum of its parts." Aristotle is credited with this quote, and it's true in the world of data. Legacy systems typically approached their role in a limited manner. Each system was intended to be used by a certain user set and handle well-defined processes and associated data. The result was a disintegrated environment with data being difficult to obtain, and frequently out of date. The parts couldn't easily cooperate to make a whole.

In the age of digital transformation, enterprise continuous testing has become critical for agile and efficient software development. But enterprises often spend too much time managing a multitude of solutions and integrations with zero cohesion between solutions and an inability to gain clear insights. It takes so much time to manage the areas around testing that there is little time for the most important part — the testing itself.

Watch how to use a multimodal LLM such as Gemini to analyze data in a BigQuery data lake and automatically generate schema layouts.

We're excited to introduce Choreo Copilot (preview), which allows you to interact with Choreo. You can pose questions in natural language and Copilot will provide answers. Choreo Copilot enables you to grasp Choreo concepts, teaches you how to perform tasks in Choreo, and provides guidance when you encounter obstacles. Copilot is familiar with APIs in Choreo’s internal marketplace. Choreo already features an AI capability that enables API testing through natural language.

In today’s subscription-driven economy, flexible billing options are crucial. While traditional post-paid models are widespread, pre-paid credit-based billing is gaining popularity. This approach empowers businesses to bill customers upfront for a set amount of usage, offering an alternative way for customers to pay for your service.