Published 09/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 34 lectures (1h 54m) | Size: 894.2 MB
Learn to Import and Clean Big Data in PySpark Work Environment, and Conduct Data Analysis using SQL queries in PySpark
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
Requirements
Simple background in data analysis
Description
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 may have a solid background in SQL language, and you have big data files you need to analyze but cannot or difficult to import these data files inside relational database engines. In this case, PySpark tool is the best solution because you can use SQL queries inside PySpark for your data analysis.
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 conducting 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 PySpark
Those who need to use SQL on Big Data
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