Learn Big Data Analysis with PySpark
Learn the most important PySpark features and how to perform data analysis using SQL queries. Import and clean big data files in PySpark for faster analysis. This course is perfect for those who need to learn data analysis in PySpark and use SQL on big data.
Learn Most Important PySpark FeaturesUnderstand Resilient Distributed DatasetLearn Most Important Python Commands and Libraries used for Data AnalysisImport Big Data Files in PySpark Work Environment and Clean themPerform Data Analysis in PySpark using SQL QueriesApache Spark is one of the most powerful tools used in big data analysis because:It’s Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.· It can run real and semi-real time data analysis.· It can handle large scale of data.· It can be run using simple code in Python programming language.You can use the easy commands in Python and SQL languages, to run data analysis on big data that cannot or difficult to import inside relational database engines. This combination of Spark, Python and SQL create a powerful work environment to analyze big data easier and faster. In this course, you will learn: What is Spark, how does it run, and how data are stored in Spark work environment. You will learn how to configure Python programming environment to run Spark code. Also, you will learn performing data analysis using real big data. In addition, you will learn to import big data files inside Python. You will learn to clean and transform data for analysis purpose. You will learn conducting business analysis using several Spark functions. You will learn to create SQL queries inside PySpark to run data analysis. After that you will learn how to interpret the results from business perspective.Who this course is for:Those who have need to learn data analysis in PySparkThose who need to use SQL on Big Data
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