If you are looking for a hands-on, complete and advanced course to learn Big Data with Apache Spark and Python, you have come to the right place. This course is designed to cover the complete skillset of Apache Spark, from RDDs, Spark SQL, Dataframes, and Spark Streaming, to Machine Learning with Spark ML, Advanced Analytics, data visualization, Spark Koalas, and Databricks. With lessons, downloadable study guides, hands-on exercises, and real-world use cases, this is the only course you’ll need to learn Apache Spark. Apache Spark has become the reference tool for Big Data, surpassing Hadoop MapReduce. Spark works up to 100 times faster than Hadoop MapReduce and has a complete ecosystem of functionalities for machine learning and data analytics. This makes Apache Spark one of the most in-demand skills for data engineers, data scientists, etc. Big Data is one of the most valuable skills today. So this course will teach you everything you need to position yourself in the Big Data job market. In this course we will teach you the complete skillset of Apache Spark and PySpark. Starting from the basics to the most advanced features. We will use visual presentations in Power Point, sharing clear explanations and useful professional advice. This course has the following sections: Introduction to big data and fundamentals of Apache SparkInstallation of Apache Spark and libraries such as Anaconda, Java, etc. Spark RDDsSpark DataframesAdvanced features with Apache SparkAdvanced analytics and data visualizationSpark KoalasMachine Learning with SparkSpark Streaming Spark GraphXDatabricksSpark in the cloud (Azure)If you’re ready to sharpen your skills, increase your career opportunities, and become a Big Data expert, join today and get immediate and lifetime access to: Complete guide to Apache Spark (PDF e-book) Downloadable Spark project files and code Hands-on exercises and quizzes Spark resources like: Cheatsheets and Summaries 1 to 1 expert support Course question and answer forum 30 days money back guaranteeSee you there!
