Snowflake is a self-managed data platform that enables users to easily store, process, analyze, and share high volumes of structured and semi-structured data. One of the most popular data platforms on the market, Snowflake has gained widespread adoption because it addresses a range of data challenges with a unified, scalable, and high-performance platform. Snowflake’s flexibility enables users to handle diverse workloads, such as data lake and data warehouse integration.

In Part 1 of this series, we looked at how Snowflake enables users to easily store, process, analyze, and share high volumes of structured and semi-structured data, as well as key metrics for monitoring compute costs, storage, and datasets. In this post, we’ll walk through how to collect and analyze these metrics using Snowsight, Snowflake’s built-in web interface.

In Part 2 of this series, we looked at Snowflake’s built-in monitoring services for compute, query, and storage. In this post, we’ll demonstrate how Datadog complements and extends Snowflake’s existing monitoring and data visualization capabilities, enabling teams to get deeper visibility and extract more valuable insights from their Snowflake data.

There’s a common misunderstanding that profiling is only useful for tiny savings that impact infra costs at scale - the so-called “milliseconds matter” approach. But by dogfooding our own profiling tools, we fixed a problem that saved tens of seconds off each user interaction with our AI agent (and for those of you who like math, that’s four orders of magnitude bigger than those milliseconds that matter).

Hello everyone! We might be a bit late, but we wanted to give everyone time to get back from the holidays. We hope you all had a fantastic break and wish you a great 2025!

Autonomous IT operations play a crucial role in enhancing the effectiveness and resilience of IT teams. Automating routine tasks and monitoring systems in real-time enables teams to respond swiftly to operational disturbances, minimizing downtime and disruptions. This proactive approach helps address issues before they escalate, fosters a more agile IT environment, and facilitates the journey to Autonomic IT.

Kubernetes (K8s) is a complex container orchestration tool, but using Terraform can simplify creating and managing a Kubernetes cluster. In this article, we'll explore how Terraform streamlines the deployment of a GKE cluster, making it easier and more efficient.

Modern enterprises face many challenges, hampering efficiency and innovation amidst tight budgets and safeguarding your brand against escalating cyber threats. Unpatched systems are also prime targets for cybercriminals who aim to access an organization’s sensitive information, intellectual property, and confidential business data. Traditionally, addressing these challenges required many point solutions, creating disjointed management.

There’s no doubt about Kubernetes’ importance for success in the cloud. It offers a cost-efficient, scalable, and automated platform for managing containerized applications while simplifying operations. Cast AI is a well-established platform specializing in Kubernetes optimization, including workload rightsizing and cluster autoscaling. But is that enough for MSPs and enterprises prioritizing cloud costs?

Exception handling is essential to.NET development, but not all exceptions are equal. Some, like NullReferenceException, surprise developers with unclear stack traces and production crashes. Others, such as MySQLException or HttpRequestException, often point to issues like resource mismanagement or network failures. At Raygun, we’ve worked with teams around the world to monitor and fix software issues, giving us deep insight into how exceptions occur and how to handle them effectively.