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06-09-2022, 05:58 AM
![[Image: b30b5d55624df0e9ef091122a46ccad8.jpeg]](https://i120.fastpic.org/big/2022/0609/d8/b30b5d55624df0e9ef091122a46ccad8.jpeg)
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.14 GB | Duration: 17 lectures • 9h 31m
Semi-Structured (JSON), Structured and Unstructured Data Analysis & Spark Performance Tuning with Spark and Python
What you'll learn
Understand Apache Spark's framework, execution and programming model for the development of Big Data Systems
Learn how to work with a free Cloud-based and a Desktop machine for Spark setup and configuration
Build advanced Big Data applications for different types of data (volume, variety, veracity) through real case studies
Learn advanced hands-on PySpark practices on structured, unstructured and semi-structured data using RDD, DataFrame and SQL
Investigate and optimize data skewness, spill and Adaptive Query Execution to tune spark performance
Requirements
Basic Python Programming
Description
In this course, students will be provided with hands-on PySpark practices using real case studies from academia and industry to be able to work interactively with massive data. In addition, students will consider distributed processing challenges, such as data skewness and spill within big data processing. We designed this course for anyone seeking to master Spark and PySpark and Spread the knowledge of Big Data Analytics using real and challenging use cases.
We will work with Spark RDD, DF, and SQL to process huge sized of data in the format of semi-structured, structured, and unstructured data. The learning outcomes and the teaching approach in this course will accelerate the learning by Identifying the most critical required skills in the industry and understanding the demands of Big Data analytics content.
We will not only cover the details of the Spark engine for large-scale data processing, but also we will drill down big data problems that allow users to instantly shift from an overview of large scale data to a more detailed and granular view using RDD, DF and SQL in real-life examples. We will walk through the Big Data case studies step by step to achieve the aim of this course.
By the end of the course, you will be able to build advanced Big Data applications for different types of data (volume, variety, veracity) and you will get acquainted with best-in-class examples of Big Data problems using PySpark.
Who this course is for
Beginner/Junior Big Data developers who want to master Spark/PySpark and Spread the knowledge of Big Data Analytics
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