Learn Big Data Analysis with PySpark
Learn Big Data Analysis with PySpark
Learn Big Data Analysis in PySpark using the Apache Spark's Powerful Features and Easy Commands of Python and SQL.
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What you'll learn
- Learn Most Important PySpark Features
- Understand Resilient Distributed Dataset
- Learn Most Important Python Commands and Libraries used for Data Analysis
- Import Big Data Files in PySpark Work Environment and Clean them
- Perform Data Analysis in PySpark using SQL Queries
Apache 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
