bestcourses is supported by learners. When you buy through links on our website, we may earn an affiliate commission. Learn more
[NEW] Best Hands-on Big Data Practices with Spark & PySpark
Semi-Structured (JSON), Structured, Unstructured Data Analysis & Distributed Processing Challenges with Spark and Python
Created by Amin Karami, offered on Udemy
To make sure that we score courses properly, we pay a lot of attention to the reviews students leave on courses and how many students are taking a course in the first place. This course has a total of 181 students which left 28 reviews at an average rating of 4.78, which is average.
We analyze course length to see if courses cover all important aspects of a topic, taking into account how long the course is compared to the category average. This course has a length of 9 hours 31 minutes, which is pretty short. This might not be a bad thing, but we've found that longer courses are often more detailed & comprehensive. The average course length for this entire category is 7 hours 54 minutes.
This course currently has a bestcourses score of 5.8/10, which makes it an average course. Overall, there are probably better courses available for this topic on our platform.
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 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.
What you will 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 to tune spark performance
- Basic Python Programming