Learning about the cluster options for parallel processing

When we have a large volume of data to process, it is not efficient and sometimes even not feasible to use a single machine with multiple cores to process the data efficiently. This is especially a challenge when working with real-time streaming data. For such scenarios, we need multiple systems that can process data in a distributed manner and perform these tasks on multiple machines in parallel. Using multiple machines to process compute-intensive tasks in parallel and in a distributed manner is called cluster computing. There are several big data distributed frameworks available to coordinate the execution of jobs in a cluster, but Hadoop MapReduce and Apache Spark are the leading contenders in this race. Both frameworks are open source projects from Apache. There are many variants (for example, Databricks) of these two platforms available with add-on features as well as maintenance support, but the fundamentals remain...