Edureka
PySpark in Action: Hands-On Data Processing

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Edureka

PySpark in Action: Hands-On Data Processing

Edureka

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
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.

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.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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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

Instructor

Edureka
Edureka
47 Courses39,943 learners

Offered by

Edureka

Recommended if you're interested in Data Analysis

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