Step 1: Download spark-2.3.2 to the local machine using the following command wget Spark-2.3.2 was the latest version by the time I wrote this article. The latest version of Apache Spark is available at
Steps to produce and consume events using Kafka-Python. 
Creating a PySpark app for consume and process the events and write back to Kafka. Starting Kafka (for more details, please refer to this article). 
Discuss the steps to perform to setup Apache Spark in a Linux environment. The article is structured in the following order In this article, I attempt to connect these dots, which are Python, Apache Spark, and Apache Kafka. To overcome all the above problems, I have identified a set of dots that could be appropriately connected. Nevertheless, the question is, ‘can this cater for real-time analytics where you need to process millions of events in a millisecond of time?’ The answer is ‘no.’ This situation is my motivation to write this article. We still have the Python micro-service library such as Flask to deploy machine-learning models and publish it as API. This is a valid argument however, we confront issues when these models are applied to production. Numerous interactions with the language we use to develop the models are required to perform experiments, and the libraries and platforms available in python to develop machine-learning models are tremendous. Here, they have a valid justification since data-driven solutions arrive with many experiments. Photo By César Gaviria from Pexels Introductionįrequently, Data scientists prefer to use Python (in some cases, R) to develop machine learning models.
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