Today, we are excited to announce the public preview of Snowflake Cortex Analyst. Cortex Analyst, built using Meta’s Llama and Mistral models, is a fully managed service that provides a conversational interface to interact with structured data in Snowflake. It streamlines the development of intuitive, self-serve analytics applications for business users, while providing industry-leading accuracy.

In today's fast-paced world of media publishing, keeping up with technological advancements and changing consumer preferences is no easy task. Tight budgets, fierce competition and evolving audience behaviors add to the pressure, creating what's often termed the "content crash" — a saturation of content that makes it hard for publishers to stand out. But amidst these challenges, there's a beacon of hope: generative AI.

How to leverage Fivetran and Starburst to supercharge your data lakehouse with end-to-end data pipelines.

In today’s fast-paced business environment, having control over your data can be the difference between success and stagnation. Leaning on Master Data Management (MDM), the creation of a single, reliable source of master data, ensures the uniformity, accuracy, stewardship, and accountability of shared data assets.

Data practitioners are consistently asked to deliver more with less, and although most executives recognize the value of innovating with data, the reality is that most data teams spend the majority of their time responding to support tickets for data access, performance and troubleshooting, and other mundane activities. At the heart of this backlog of requests is this: data is hard to work with, and it’s made even harder when users need to work to get or find what they need.

Broadly defined, the supply chain management process (SCM) refers to the coordination of all activities amongst participants in the supply chain, such as sourcing and procurement of raw materials, manufacturing, distribution center coordination, and sales.

The modern data-driven approach comes with a host of benefits. A few major ones include better insights, more informed decision-making, and less reliance on guesswork. However, some undesirable scenarios can occur in the process of generating, accumulating, and analyzing data. One such scenario involves organizational data scattered across multiple storage locations. In such instances, each department’s data often ends up siloed and largely unusable by other teams.

See a solution that bridges the gap between gRPC services and RESTful HTTP APIs, and is enhanced with Apigee’s enterprise API management platform.

Data is the driving force of the world’s modern economies, but data teams are struggling to meet demand to support generative AI (GenAI), including rapid data volume growth and the increasing complexity of data pipelines. More than 88% of software engineers, data scientists, and SQL analysts surveyed say they are turning to AI for more effective bug-fixing and troubleshooting. And 84% of engineers who use AI said it frees up their time to focus on high-value activities.