In the wake of the Microsoft-CrowdStrike incident on July 19, 2024, Squadcast community has been actively reflecting on the lessons learned from this disruptive event. This global outage, affecting 8.5 million Windows machines, has served as a critical case study for incident management and operational resilience.

In software development, load testing plays a critical role in ensuring that applications perform optimally under any imaginable load condition. To do this, developers subject applications to several types of load tests, including scalability, spike, endurance, and stress testing. The ultimate goal of these performance tests is to pinpoint potential bottlenecks and ensure the reliability of the overall system where the software application runs before reaching production.

Modern SecOps teams use Security Information and Event Management (SIEM) software to aggregate security logs, detect anomalies, hunt for threats, and enable rapid incident response. While SIEMs enable accurate, near real-time detection of threats, today's SIEM solutions were never designed to handle the volume of security data organizations generate daily. As daily log ingestion grows, so do the costs of data management.

Improving operational efficiency and reducing the time it takes to resolve incidents are big goals. New options to customize your incident feed view in BigPanda allow you to highlight the most relevant context upfront, making a big difference. Reducing data visibility issues and redundant data can give operators greater control. The BigPanda Incident 360 Console is where ITOps teams and NOC operators receive the first notification and ongoing updates for all incidents.

Touching every life across the population—right down to the last mile—city councils provide digital access to essential services and information systems and ensure they are easily accessible to safeguard civic well-being, law and order, and quality of life for everyone.

GitOps is a framework that combines the principles of software development and continuous integration, continuous delivery (CI/CD) to automate and manage application configurations and infrastructure using Git.

In this blog series we’ve examined Five Myths of Apache Spark Optimization. But one final, bonus myth remains unaddressed: Bonus Myth: I’ve done everything I can. The rest of the application waste is just the cost of running Apache Spark. Unfortunately, many companies running cloud environments have come to think of application waste as a cost of doing business, as inevitable as rent and taxes.

The days of monolithic applications and simple monitoring tools are gone. With the arrival of public and private cloud infrastructure and hyperconnectivity on Edge devices, organizations struggle to scale their applications, identify issues before it affects their customers, and to maintain their SLA s. Enter a pplication p erformance m onitoring(APM), a game-changer in the realm of IT operations.

The advancements in GenAI technology are creating a significant impact across domains/sectors, and the Kubernetes ecosystem is no exception. Numerous interesting GenAI projects and products have emerged aimed at enhancing the efficiency of Kubernetes cluster creation and management. From simplifying application containerization for engineers to addressing complex Kubernetes-related queries or troubleshooting issues within a cluster, GenAI demonstrates immense potential.

When enterprises transition to a microservices model, they often need to migrate their legacy applications to the new infrastructure. One popular framework used for these traditional applications is.Net. Due to migration, enterprises require the ability to run Windows containers in their Kubernetes infrastructure.