In 2017, we open sourced Grafana k6 and made its first beta available to everyone. This wasn’t our first rodeo — k6 marked the third load testing tool our team had developed over a decade. We had recognized the gaps in existing solutions, as well as the barriers that were hindering adoption in the developer community. The plan was simple yet ambitious: let’s build a tool developers actually enjoy using and that helps engineering teams build more reliable software.

Today’s evolving digital landscape requires both hybrid cloud and multi-cloud strategies to drive efficiency, innovation, and scalability. But this means more complexity and a unique set of challenges for network and cloud engineers, particularly when it comes to managing and gaining visibility across these environments.

Dissecting the RCA of Alerting - Reliability, Correlations, Actionability.

The internet serves as the backbone for communication, collaboration, and access to information in today’s digital world. However, despite its widespread use and importance, the internet is not immune to reliability issues. From occasional slowdowns to complete outages, internet users often encounter disruptions that can impact their productivity and connectivity. Several factors contribute to internet unreliability.

With the advent of microservices and cloud native, organizations are shifting how they approach software development and deployment to become more agile and respond quickly to continually evolving business needs. These changes result in fundamental transformation for IT.

With users today expecting speed, reliability, and responsiveness from every application they use, the delivery of seamless experiences across various platforms becomes essential for organizations. Caching solutions like Redis play a vital role in these ecosystems by storing frequently accessed data in memory, reducing the need to retrieve it from slower back-end systems, such as databases.

Machine learning (ML) platforms such as Amazon Sagemaker, Azure Machine Learning, and Google Vertex AI are fully managed services that enable data scientists and engineers to easily build, train, and deploy ML models. Common use cases for ML platforms include natural language processing (NLP) models for text analysis and chatbots, personalized recommendation systems for e-commerce web applications and streaming services, and predictive business analytics.