Using Apache Spark Docker containers to run pyspark programs using spark-submit

<h1><strong>What is Spark? &mdash; A quick overview</strong></h1> <p>Apache Spark is an open-source big data processing framework designed to process and analyze large datasets in a distributed and efficient manner. It provides a faster and more flexible alternative to MapReduce, which was primarily used for batch processing. Spark&rsquo;s distributed computing model processes data in parallel across multiple nodes in a cluster, and its high-level programming interface simplifies the development of data processing applications. Spark&rsquo;s speed, flexibility, and ease of use have made it a popular choice for big data processing and analysis in a variety of use cases.</p> <h1>Spark set up &mdash; A problem?</h1> <p>Setting up Spark locally is feasible for smaller datasets and simpler use cases, but as data size and application complexity increase, a local setup may not have sufficient resources to handle the load. A local setup may also lack fault tolerance and redundancy features of a distributed setup, leading to potential data loss or downtime. Furthermore, scaling the Spark cluster dynamically to handle varying workloads can be challenging to achieve with a local setup. A distributed cluster setup with sufficient resources would be more appropriate for larger and more complex use cases that require high performance, scalability, and fault tolerance.</p> <p><a href="https://medium.com/@mehmood9501/using-apache-spark-docker-containers-to-run-pyspark-programs-using-spark-submit-afd6da480e0f"><strong>Read More</strong></a></p>