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.
By the end of this course, you will be able to:
- Explore foundational concepts of Big Data and the components of the Hadoop ecosystem
- Explain the architecture and key principles underlying Apache Spark
- Utilize RDD transformations and actions to process large-scale datasets with PySpark
- Execute advanced DataFrame operations, including handling complex data types and performing aggregations
- Evaluate and enhance data processing workflows by leveraging PySpark SQL and advanced DataFrame techniques
This course is ideal for learners who are new to data engineering and want to understand how to use PySpark effectively.
Basic knowledge in Python is recommended, but no prior experience with PySpark is necessary.
Start your journey with PySpark and build a strong foundation in distributed data processing!
Overview
Syllabus
- Big Data Processing with PySpark
- 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.
- Working with RDD
- 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.
- PySpark DataFrames
- 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.
- PySpark SQL
- 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.
- Course Wrap Up and Assessment
- 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.
Taught by
Edureka
