Businesses need to stay agile and make data-driven decisions in real time to outperform their competitors. Real-time analytics is emerging as a game-changer, with 80% of companies showing an increase in revenue due to real-time data analytics as companies can gain valuable insights on the fly. This blog post will explore the concept of real-time analytics, its examples, and some challenges faced when implementing it. Read on for a detailed explanation of this exciting area in data analytics.

We often get questions like: And while the 14-year-old in me is proud to say that we’ve done 24/7 support for clusters of 1000+ nodes holding many PB of data, I am quick to add that.

Imagine you’re piloting a spaceship through the cosmos, embarking on a thrilling journey to explore the far reaches of the universe. As the captain of this ship, you need a dashboard that displays critical information about your vessel, such as fuel levels, navigation data, and life support systems. This dashboard is your lifeline, providing you with real-time insights about the health and performance of various systems within your ship, so you can quickly make critical decisions.

Navigating the realm of Windows observability often referred to as O11y (short for observability), can be a complicated journey. Windows environments are known for their complexity, with various services, applications, and workloads running on each host.

Kubernetes (K8s) is at the forefront of modern infrastructure, but with its capabilities comes a deluge of telemetry data. Efficiently managing and optimizing this data is crucial to harnessing the full potential of your Kubernetes deployments.

In today's digital landscape, Observability and telemetry data play a crucial role in ensuring the performance, reliability, and security of modern applications and services. However, as data volumes explode due to the proliferation of micro-services, cloud-based applications, and connected devices, existing architectures are hitting a scalability wall.