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.

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.

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.

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.

In our last post, we explored how Dependency Injection (DI) is a powerful design pattern that can improve our ExUnit tests. In this article, we will dive deeper into the topic of DI in Elixir, focusing on the Rewire library for Elixir projects. We will cover Rewire's core concepts, how to get started with it, and practical examples. We will also see how to use Rewire alongside Mox. Let's get started!

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.