Artificial Intelligence (AI) is primed to reshape the way just about every business operates. Cloudera research projected that more than one third (36%) of organizations in the U.S. are in the early stages of exploring the potential for AI implementation. But even with its rise, AI is still a struggle for some enterprises. AI, and any analytics for that matter, are only as good as the data upon which they are based. And that’s where the rub is.

Managing complex data pipelines is a major challenge for data-driven organizations looking to accelerate analytics initiatives. While AI-powered, self-service BI platforms like ThoughtSpot can fully operationalize insights at scale by delivering visual data exploration and discovery, it still requires robust underlying data management. Now, that’s changing. Snowflake's new dynamic tables feature redefines how BI and analytics teams approach data transformation pipelines.

As Large Language Models are revolutionizing natural language prompts, Large Vision Models (LVMs) represent another new, exciting frontier for AI. An estimated 90% of the world’s data is unstructured, much of it in the form of visual content such as images and videos. Insights from analyzing this visual data can open up powerful new use cases that significantly boost productivity and efficiency, but enterprises need sophisticated computer vision technologies to achieve this.

In this article, we will walk you through the process of implementing fine grained access control for the data governance framework within the Cloudera platform. This will allow a data office to implement access policies over metadata management assets like tags or classifications, business glossaries, and data catalog entities, laying the foundation for comprehensive data access control.

Did you know that approximately two thirds of Snowflake customers capture the latitude and longitude of some business entity or event in their account? While latitude and longitude columns can often be used by BI tools and Python libraries to plot points on a map, or shade common administrative boundaries such as states, provinces and countries, companies can do so much more with this valuable geospatial data to perform complex analyses.

In my first post of this new blog series, I introduced the concept of “sliding doors” for creating value with data: the divergent paths that organizations can take which can lead to great - or not so great - outcomes. In this post, I want to explore this for what has become a new data imperative for organizations today: effectively reporting their environmental, social and governance (ESG) data to meet new regulations.

BigQuery now supports generative AI use cases using Gemini 1.0 Pro with Vertex AI, providing higher input/output scale and better result quality.

Businesses often need to aggregate topics because it is essential for organizing, simplifying, and optimizing the processing of streaming data. It enables efficient analysis, facilitates modular development, and enhances the overall effectiveness of streaming applications. For example, if there are separate clusters, and there are topics with the same purpose in the different clusters, then it is useful to aggregate the content into one topic.

Welcome back to Yellowfin Japan’s ‘How to?’ blog series! In our previous blog, we went through how to capture data using Yellowfin's Data Transformation flow, and the preparation and steps for creating reports using Yellowfin View. It may seem like a lot of simple work, but as the number of reports to be created increases, the importance of data preparation becomes far more apparent. So, what about after you’ve done all the setup? Well, it’s now time to create reports!

Snowflake Marketplace is a vibrant resource, with hundreds of providers offering thousands of ready-to-try or ready-to-buy third-party data sets, applications and services. Many of these providers make their products available on Snowflake Marketplace for Snowflake customers to purchase — and they use our integrated Marketplace Monetization capabilities to simplify the process and speed up procurement and sales cycles.