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!
Key Concepts of RDD
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Skills You'll Learn
SQL Querying with PySpark, RDD Transformations, Data Processing, Advanced DataFrame Operations, Data Engineering
From the lesson
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.
Taught By
Edureka