PySpark for Beginners
Take your first steps in developing large-scale distributed data processing applications using Apache Spark and Python
Created by Tomasz Drabas
Explore how to harness the power of Apache Spark and Python to process large-scale data efficiently. Gain practical experience with Spark's core features, including data abstraction, streaming, and machine learning. By the end, you'll know how to build and deploy scalable data applications using PySpark.
Packt | Jun 2018 | 94 min
What You Will Learn
You'll start by setting up your Spark environment and learning the basics of Spark architecture. As you progress, you'll work hands-on with RDDs, DataFrames, and Spark SQL, then move into streaming and machine learning tasks. Each topic builds on the last, so you can see how the pieces fit together in real-world scenarios.
Key Features
- Set up Spark with Python and work confidently with RDDs and DataFrames
- Apply Spark SQL for data analysis and build machine learning models with MLlib
- Deploy scalable data processing applications to the cloud using spark-submit
Target Audience
Ideal for Python developers ready to expand into distributed data processing and analytics. If you have a solid grasp of Python and want to build scalable data solutions with Spark, you'll find practical guidance here. No prior Spark experience is required, but basic familiarity will help you move faster.
