AirflowĪpache Airflow is an open-source platform designed for developing, scheduling, and monitoring batch-oriented workflows. Spark is designed to be a fast and versatile engine for large-scale data processing. It is an open-source platform that leverages in-memory caching and optimized query execution to deliver fast queries on data of any size. SparkĪpache Spark is a distributed processing system for handling big data workloads. Google Kubernetes is a highly flexible container tool to consistently deliver complex applications running on clusters of hundreds to thousands of individual servers. Kubernetes helps to manage containerized applications in various types of physical, virtual, and cloud environments. Kubernetes is a container management system developed on the Google platform. Uses a common k8s ecosystem as with other workloads and offers features such as continuous deployment, role-based access control (RBAC), dedicated node-pools, and autoscaling, among others.īefore moving to the setup part, let’s first have a quick look at all the technologies that will be covered ahead:.Ad-hoc monitoring for better visibility into the system’s performance.Cost-effectiveness by not relying on a specific cloud provider.This approach saves time in orchestrating, distributing, and scheduling Spark jobs across different cloud providers Portability to any cloud environment, making it less dependent on any particular cloud provider.Monitoring compute nodes and automatically replaces instances in case of failure, ensuring reliability.Scalability to meet any workload demands.In addition, deploying Spark on K8s solution could offer some benefits to the business: Kubernetes can save effort and provide a better experience while executing Spark jobs. The goal is to enable data engineers to program the stack seamlessly for similar workloads and requirements. This blog will detail the steps for setting up a Spark App on Kubernetes using the Airflow scheduler. Ultimately, success with Spark on Kubernetes depends on the ability to monitor and manage the platform effectively. This means having a strong understanding of their infrastructure and being able to optimize its performance across multiple dimensions. Enterprises that choose to run Spark with Kubernetes must be prepared to tackle the challenges that come with this solution. However, it’s important to note that this approach does have its drawbacks. As more and more businesses migrate to the cloud, the number of companies deploying Spark on Kubernetes continues to rise. In fact, a recent survey states that 96% of organizations are now either using or evaluating Kubernetes. The benefits that K8s offer have been the driving force behind this trend. In recent years, there has been a significant surge in companies using Spark on Kubernetes (K8s) and it’s no wonder why.
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