Analytics

In the current constantly evolving hybrid cloud enterprise business environment, reliable and efficient infrastructure services for consulting, implementation and operations are critical for organizations to thrive. Whether you're a small startup or a large enterprise, choosing the right infrastructure services provider can significantly impact your operations, scalability, and overall success. Here’s what to look for in an enterprise infrastructure services provider.

Cyber attacks are not going away. In fact, the increased frequency in the past couple of years is startling. There were 2,365 breaches in the U.S. in 2023 with 343.3M victims. This represented a 72% increase since 2021, which held the previous all-time record. And the numbers are only expected to grow. Organizations are rightly concerned about their own preparedness and ability to stay operational in the face of these threats.

Learn how to use BigQuery DataFrames together with LLMs to generate synthetic data right inside of BigQuery.

Of all of the emerging tech of the last two decades, artificial intelligence (AI) is tipping the hype scale, causing organizations from all industries to rethink their digital transformation initiatives asking where it fits in. In Financial Services, the projected numbers are staggering. According to a recent McKinsey & Co.

Last year at Summit, we announced the public launch of the Snowflake Performance Index (SPI), an aggregate index for measuring real-world improvements in Snowflake performance experienced by customers over time.

It is estimated that between 80% and 90% of the world’s data is unstructured1, with text files and documents making up a significant portion. Every day, countless text-based documents, like contracts and insurance claims, are stored for safekeeping. Despite containing a wealth of insights, this vast trove of information often remains untapped, as the process of extracting relevant data from these documents is challenging, tedious and time-consuming.

More than two-thirds of companies are currently using Generative AI (GenAI) models, such as large language models (LLMs), which can understand and generate human-like text, images, video, music, and even code. However, the true power of these models lies in their ability to adapt to an enterprise’s unique context. By leveraging an organization’s proprietary data, GenAI models can produce highly relevant and customized outputs that align with the business’s specific needs and objectives.