In response to the many disruptions across supply chains over the past year, new trends are emerging and others are gaining more traction to help bolster resilience in this space. Organizations are pivoting operations, leveraging different resources, and taking advantage of new technologies to streamline their supply chain orchestration moving forward.

Heroku is a cloud platform as a service (PaaS) for efficiently building, deploying, monitoring, and scaling applications. Originally created to work with the Ruby programming language, Heroku is now part of the Salesforce platform and supports languages such as Java, Node.js, PHP, Python, and Scala. While Heroku makes it easy to develop production-ready applications fast, one question remains: how can you integrate your Heroku app data with the rest of your data infrastructure and workflows?

Apache Spark is now widely used in many enterprises for building high-performance ETL and Machine Learning pipelines. If the users are already familiar with Python then PySpark provides a python API for using Apache Spark. When users work with PySpark they often use existing python and/or custom Python packages in their program to extend and complement Apache Spark’s functionality. Apache Spark provides several options to manage these dependencies.

In this blog post we are going to look at several JMeter Controllers, specifically: This is not an exhaustive list of controllers that JMeter offers but these once will give you a clear insight into how controllers are integral in defining load testing scenarios and how without them you will struggle to build complex and indicative load tests.