Learn why successful AI initiatives require a well-executed data foundation to drive business value.

Today, as businesses invest approximately 23% of their annual IT budget in QA and testing, the field of QA is undergoing a transformative shift. QA teams are often tasked with developing comprehensive test plans based on application development methodologies, architecture styles, frameworks, and other factors. However, for QA teams to develop better-quality software, they need to have the right mindset rather than simply enforcing rigid review processes.

Minimum Viable Architecture plays a critical role in the success of your MVP. Learn how to avoid the most common pitfalls in implementing this strategic approach.

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.

Gartner predicts that generative AI will be a workforce partner for 90% of companies globally by 2025. However, Alexander Bant, Gartner Finance’s chief of research, expressed a contrasting view at the Gartner CFO & Finance Executive Conference in Sydney in February 2024. He noted, “The large majority of CFOs continue to be displeased with the performance of digital investments across their organization.”

Securing access to external resources within private networks—whether in other public clouds or on-premise data centers—is a key use case for applications and APIs deployed on the Choreo cloud data planes. Today, we are simplifying the process of creating and managing secure VPN connections from the Choreo cloud data planes to any external network with Tailscale, a renowned and trusted WireGuard VPN provider.

Black box testing is a testing method where testers evaluate the quality of a system without knowledge of its internal structures. The system is a “black box”: they know what it does, but not how it achieves those results. In this article, we’ll learn more about black box testing in-depth, the common techniques used, and black box testing best practices.

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.