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.

Resilient and reliable IT systems have become a minimum requirement for modern businesses—a fact driven home by any number of high-profile outages over the past few years. Unfortunately, when those outages are in the financial sector, it can have far-reaching and incredibly damaging results.

Operations are the backbone of successful software delivery, but the specifics of each phase—Day-0, Day-1, and Day-2—often get overlooked. Understanding these phases can help you streamline deployments, reduce risks, and maintain robust, scalable systems. Let’s break down what each phase entails and explore their distinct activities, tools, and best practices.

When it comes to monitoring and observability, Splunk and Prometheus are two prominent tools with distinct strengths. Splunk excels in enterprise-level security and observability, while Prometheus is known for its efficient handling of time-series data. In this blog, I have compared these two tools, focusing on their unique features, and strengths. Remember, some insights may reflect personal preferences, helping you find the best fit for your specific monitoring needs.