According to a study by Statista, the cloud storage market was valued at $90.17 billion in 2022 and will reach a value of $472.47 billion by 2030. These figures indicate a growing shift toward cloud computing and data storage solutions. A typical scenario in modern data management involves data transfer from cloud storage to cloud-based computing platforms. Amazon’s Simple Storage Service (S3) is among the go-to options for the former, and businesses trust Snowflake for the latter.

In this blog series, I am exploring the “sliding doors”, or divergent paths, for creating value with data across different use cases, practices, and strategies. In this post, I want to discuss how to generate value with Data Products. As I reviewed in my last blog, grabbing the door to the better path for managing your data isn’t just about solving your particular use case: it’s ultimately about delivering value for your business.

Snowflake is committed to helping our customers unlock the power of artificial intelligence (AI) to drive better decisions, improve productivity and reach more customers using all types of data. Large Language Models (LLMs) are a critical component of generative AI applications, and multimodal models are an exciting category that allows users to go beyond text and incorporate images and video into their prompts to get a better understanding of the context and meaning of the data.

Having frequently worked with governments around the world over the course of my career, I’ve had all kinds of discussions about the global impact of generative AI. Today, I’m publicly wading into those waters to deliver my perspective, and my opinion is that … it’s incredibly hard to predict the future. Done. Wrapped up this entire post in a single sentence.

Introducing new BigQuery SQL features for performing two of the most common time-series operations: windowing and gap filling.

Data engineers and architects are being asked to do more with their enterprise data than ever before. Yet, the knowledge gap between what businesses want to do with data and how they can accomplish it is growing daily—especially considering today's AI hype cycle. With all that noise in the market, it's easy to see how organizations struggle to keep pace with innovation.

Palo Alto Networks used BigQuery ML to automatically identify and label the owner, environment, and cost center for each of its cloud projects.

Underperforming AI programs/models built using low-quality or inaccurate data cost companies up to 6% of annual revenue on average.

Cloud Functions will revolutionize your data pipeline.

Executives across various industries are under pressure to reach insights and make decisions quickly. This is driving the importance of streaming data and analytics, which play a crucial role in making better-informed decisions that likely lead to faster, better outcomes.