Discover how to use Spark within AIOps and with Apache Spark application submission, including use of Spark’s unified interface, ‘spark-submit’, describe and apply options for submitting applications, identify external application dependency management techniques and list Spark Shell benefits. Next, learn about Apache Spark application submission, including use of Spark’s unified interface, ‘spark-submit’ and learn about options and dependencies. Because Spark application work happens on the cluster, you need be able to identify Apache Cluster Managers, their components, benefits, and know how to connect with each cluster manager and how and when you might want to set up a local, standalone Spark instance. Learn how you can track work using the Spark Application UI. ![]() In this course, you will also learn about Resilient Distributed Datasets, or RDDs, that enable parallel processing across the nodes of a Spark cluster.Įxplore how Spark processes the requests that your application submits. The course provides an overview of the platform, going into the different components that make up Apache Spark. In this course, you will discover how to leverage Spark to deliver reliable insights. It is an open-source processing engine built around speed, ease of use, and analytics. Hive, a data warehouse software, provides an SQL-like interface to efficiently query and manipulate large data sets residing in various databases and file systems that integrate with Hadoop.Īpache Spark is an open-source processing engine that provides users new ways to store and make use of big data. Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. You’ll explore how Hadoop and Hive help leverage the benefits of Big Data while overcoming some of the challenges it poses. You will gain an understanding about the features, benefits, limitations, and applications of some of the Big Data processing tools. In this course, you will learn about the characteristics of Big Data and its application in Big Data Analytics. ![]() ![]() Bernard Marr defines Big Data as the digital trace that we are generating in this digital era.
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