PySpark in Action: Hands-on Data Processing is a foundational course designed to help you begin working with PySpark and distributed data processing. You will explore the essential concepts of Big Data, Hadoop, and Apache Spark, and gain practical experience using PySpark to process and analyze large datasets. Through hands-on exercises, you will work with RDDs, DataFrames, and SQL queries in PySpark, giving you the skills to manage data at scale.
PySpark in Action: Hands-On Data Processing
PySpark in Action: Hands-On Data Processing
This course is part of PySpark for Data Science Specialization
Instructor: Edureka
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
15 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Explore the fundamental concepts of Big Data and the components of the Hadoop ecosystem.
Explain the architecture and key principles of Apache Spark and its role in big data processing.
Utilize RDD transformations and actions to effectively process large-scale datasets with PySpark.
Execute advanced DataFrame operations, including data manipulation and aggregation techniques.
Skills you'll gain
Details to know
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Recently updated!
October 2024
Assessments
17 assignments
Taught in English
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This course is part of the PySpark for Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course
This module introduces you to the fundamental concepts of Big Data and Hadoop. You will explore the Hadoop ecosystem, its components, and the Hadoop Distributed File System (HDFS), setting the foundation for understanding big data processing and storage solutions.
What's included
15 videos5 readings4 assignments1 discussion prompt
Dive into the core of PySpark by learning about Resilient Distributed Datasets (RDDs). This module covers the fundamentals of RDDs, how they work, and their key transformations and actions, enabling efficient distributed data processing in PySpark.
What's included
25 videos4 readings4 assignments3 discussion prompts
This module covers the creation and manipulation of DataFrames in PySpark. You will learn how to perform basic and advanced operations, including aggregation, grouping, and handling missing data, with a focus on optimizing large-scale data processing tasks.
What's included
22 videos4 readings4 assignments1 discussion prompt
In this module, you will explore the SQL capabilities of PySpark. Learn how to perform CRUD operations, execute SQL commands, and merge and aggregate data using PySpark SQL. You'll also discover best practices for using SQL with PySpark to enhance data workflows.
What's included
28 videos4 readings4 assignments2 discussion prompts
This module is meant to test how well you understand the different ideas and lessons you've learned in this course. You will undertake a project based on these PySpark concepts and complete a comprehensive quiz that will assess your confidence and proficiency in Data Processing with PySpark.
What's included
1 video1 reading1 assignment1 discussion prompt
Recommended if you're interested in Data Analysis
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Frequently asked questions
You will need access to a computer with Python and Apache Spark installed. Detailed setup instructions will be provided at the beginning of the course.
This course is designed for individuals new to big data and PySpark, providing a solid foundation to start working with distributed data processing.
While prior SQL knowledge is beneficial, it is not mandatory. The course will introduce SQL concepts as they relate to PySpark and provide practice with SQL queries.