Leverage machine and deep learning models to build applications on real-time data using PySpark. This book is perfect for those who want to learn to use this language to perform exploratory data analysis and solve an array of business challenges.
You'll start by reviewing PySpark fundamentals, such as Spark's core architecture, and see how to use PySpark for big data processing like data ingestion, cleaning, and transformations techniques. This is followed by building workflows for analyzing streaming data using PySpark and a comparison of various streaming platforms.
You'll then see how to schedule different spark jobs using Airflow with PySpark and book examine tuning machine and deep learning models for real-time predictions. This book concludes with a discussion on graph frames and performing network analysis using graph algorithms in PySpark. All the code presented in the book will be available in Python scripts on Github.
What You'll Learn- Develop pipelines for streaming data processing using PySpark
- Build Machine Learning & Deep Learning models using PySpark latest offerings
- Use graph analytics using PySpark
- Create Sequence Embeddings from Text data
Who This Book is For Data Scientists, machine learning and deep learning engineers who want to learn and use PySpark for real time analysis on streaming data.
Inhaltsverzeichnis
Chapter 1: Introduction to PySpark. - Chapter 2: Data Processing. - Chapter 3: Spark Structured Streaming. - Chapter 4: Airflow. - Chapter 5: Machine Learning Library (MLlib). - Chapter 6: Supervised Machine Learning. - Chapter 7: Unsupervised Machine Learning. - Chapter 8: Deep Learning Using PySpark.
