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PySpark in Action: Hands-On Data Processing
This course is part of PySpark for Data Science.
Course Cost
Free course
Intermediate
Skill Level
13 Hours
Self-paced lessons
PySpark in Action: Hands-on Data Processing is a comprehensive course designed for individuals looking to master distributed data processing with Apache Spark's Python API. This intermediate-level program takes you through the essential concepts of Big Data and the Hadoop ecosystem before diving into the architecture and principles of Apache Spark. Through hands-on exercises, you'll gain practical experience working with Resilient Distributed Datasets (RDDs), learning key transformations and actions that enable efficient processing of large-scale data. The course also covers advanced DataFrame operations, including data manipulation, aggregation techniques, and handling complex data types. You'll explore PySpark SQL capabilities for structured data processing and learn data visualization techniques to effectively present your findings. By the end of this course, you'll have the skills to process and analyze large datasets, optimize data workflows, and implement distributed computing solutions using PySpark.
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
Perform SQL queries and CRUD operations using PySpark SQL
Visualize data effectively using various Python libraries
Implement best practices for optimizing PySpark workflows
Apply PySpark concepts to real-world data analysis scenarios
Skills you'll gain
This course includes:
7.5 Hours PreRecorded video
17 assignments
Access on Mobile, Tablet, Desktop
Batch access
Shareable certificate
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There are 5 modules in this course
This course provides a comprehensive introduction to PySpark for distributed data processing. Students begin by exploring the fundamental concepts of Big Data and the Hadoop ecosystem, establishing a solid foundation for understanding large-scale data solutions. The curriculum progresses through the architecture and key principles of Apache Spark before diving into hands-on work with Resilient Distributed Datasets (RDDs), teaching essential transformations and actions for efficient data processing. Learners then advance to PySpark DataFrames, mastering creation, manipulation, and complex operations including aggregations and handling missing data. The course also covers PySpark SQL capabilities, allowing students to perform structured data queries and CRUD operations. Throughout the program, practical exercises and real-world examples reinforce learning, culminating in a capstone project that applies all concepts to analyze furniture sales data.
Big Data Processing with PySpark
Module 1 · 2 Hours to complete
Working with RDD
Module 2 · 3 Hours to complete
PySpark DataFrames
Module 3 · 3 Hours to complete
PySpark SQL
Module 4 · 3 Hours to complete
Course Wrap Up and Assessment
Module 5 · 1 Hours to complete
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Faculties
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