Introduction to PySpark

Intermedio

Actualizado 5/2025

Master PySpark to handle big data with ease—learn to process, query, and optimize massive datasets for powerful analytics!

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Descripción del curso

This course is perfect for data engineers, data scientists, and machine learning practitioners looking to work with large datasets efficiently. Whether you're transitioning from tools like Pandas or diving into big data technologies for the first time, this course offers a solid introduction to PySpark and distributed data processing.

Why Spark? Why Now?

Discover the speed and scalability of Apache Spark, the powerful framework designed for handling big data. Through interactive lessons and hands-on exercises, you'll see how Spark's in-memory processing gives it an edge over traditional frameworks like Hadoop. You'll start by setting up Spark sessions and dive into core components like Resilient Distributed Datasets (RDDs) and DataFrames. Learn to filter, group, and join datasets with ease while working on real-world examples.

Boost Your Python and SQL Skills for Big Data

Learn how to harness PySpark SQL for querying and managing data using familiar SQL syntax. Tackle schemas, complex data types, and user-defined functions (UDFs), all while building skills in caching and optimizing performance for distributed systems.

Build Your Big Data Foundations

By the end of this course, you'll have the confidence to handle, query, and process big data using PySpark. With these foundational skills, you'll be ready to explore advanced topics like machine learning and big data analytics.

Prerrequisitos

Introduction to SQL Data Manipulation with pandas

Introduction to Apache Spark and PySpark

Descripción del curso

Why Spark? Why Now?

Boost Your Python and SQL Skills for Big Data

Build Your Big Data Foundations

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