Distributed computing is a field of computer science that focuses on the design and development of systems and algorithms that can be run across multiple computers, or “nodes,” in a network. In a distributed system, each node can perform a specific task or set of tasks, and the results of those tasks can be shared and combined with the results from other nodes in the network. This allows distributed systems to handle large amounts of data and perform complex computations more efficiently than a single computer could. Distributed systems are commonly used in applications such as data analytics, scientific simulations, and cloud computing, where the ability to distribute workloads across multiple nodes can greatly improve performance and scalability.
Apache Spark is a popular distributed computing framework for Big Data processing and analytics. In this tutorial, we will work hands-on with PySpark, Spark’s Python-specific interface. We built on the conceptual knowledge gained in a previous tutorial: Introduction to BigData Analytics with Apache Spark, in which we learned about the essential concepts behind Apache Spark … Read more
Apache Spark is an absolute powerhouse when it comes to open-source Big Data processing and analytics. It’s used all over the place for everything from data processing to machine learning to real-time stream processing. Thanks to its distributed architecture, it can parallelize workloads like nobody’s business, making it a lean, mean data processing machine when … Read more
