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

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.

The advent of digital technologies requires industries, including shipping services, to meet the demands of global e-commerce. Accurate tracking, efficient inventory management, and seamless deliveries are now essential components that define successful shipping providers. Shipping providers turn to advanced tools to navigate the complexities of contemporary logistics. These solutions help automate workflows, minimize delays, and optimize customer service.

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.

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.

Due to their ability to enable parallelism and asynchronous execution, threads have an essential role in efficiently utilizing multi-core processors. Without them, handling concurrent tasks in modern applications like real-time inference in IoT or Asynchronous I/O in AI/ML would neither be feasible nor imaginable. The arrival of virtual threads has further grown such possibilities by eliminating the sole dependency on operating system threads.

The value proposition of embedded analytics in 2024 has never been clearer: real-time insights that help your user base make smarter decisions, while enhancing their engagement with both data analytics as a tool, and your product or business systems. But what about the costs?

Bringing machine learning (ML) models into production is often hindered by fragmented MLOps processes that are difficult to scale with the underlying data. Many enterprises stitch together a complex mix of various MLOps tools to build an end-to-end ML pipeline. The friction of having to set up and manage separate environments for features and models creates operational complexity that can be costly to maintain and difficult to use.