One of our most commonly asked questions is when we will support Worker Groups in Azure. We’ve heard you loud and clear; some exciting news will make your data management much more straightforward. We’re introducing a Cribl-managed Cribl Stream data plane, also known as Worker Groups, in Microsoft Azure. These Worker Groups are oil to your engine—essential for data operations, handling everything from shaping and transforming to enriching and processing your data.

Azure App Registration is registering an application with Azure Entra ID to access the required Azure resources securely.

Cloud observability tools offer visibility into your cloud infrastructure. They collect data from various sources to help you understand your applications’ performance. The tools enable you to monitor, optimize, and troubleshoot your cloud environment. In this guide, we’ll share why cloud observability is important and the best tools to consider.

While major outages like the recent CrowdStrike incident dominate headlines, those of us in the trenches ensuring Internet Resilience know that most of our issues are not necessarily global but localized by geography, autonomous systems, or something else. Micro-outages – those elusive, localized incidents – can pose the most persistent threat to observability.

AWS Transit Gateway costs are multifaceted and can get out of control quickly. In this post, discover how Kentik can help you understand and control the network traffic driving AWS Transit Gateway costs. Learn how Kentik can help you understand traffic patterns, optimize data flows, and keep your Transit Gateway costs in check.

Developing web-based applications often requires a robust, scalable, and efficient database solution that can handle dynamic data needs. Azure SQL Server provides developers with a cloud-based platform that fulfills these requirements. By combining Azure SQL Server with modern web development technologies, developers can build powerful, data-driven web applications that offer distinctive user experiences.

In this blog series we’re examining the Five Myths of Apache Spark Optimization. The fifth and final myth in this series relates to another common assumption of many Spark users: Spark Dynamic Allocation automatically prevents Spark from wasting resources.