About the course
Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0
Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This course will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this course, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.
All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/PySpark-for-Beginners
Style and Approach
This course takes a very comprehensive, step-by-step approach so you understand how the Spark ecosystem can be used with Python to develop efficient, scalable solutions. Every section is standalone and defined in a very easy-to-understand manner.
What You Will Learn
- Learn about Apache Spark and the Spark 2.0 architecture
- Build and interact with Spark DataFrames using Spark SQL
- Read, transform, and understand data and use it to train machine learning models
- Build machine learning models with MLlib and ML
Tomasz Drabas is a Data Scientist working for Microsoft and currently residing in the Seattle area. He has over 13 years of experience in data analytics and data science in numerous fields: advanced technology, airlines, telecommunications, finance, and consulting he gained while working on three continents: Europe, Australia, and North America. While in Australia, Tomasz has been working on his PhD in Operations Research with a focus on choice modeling and revenue management applications in the airline industry.
At Microsoft, Tomasz works with big data on a daily basis, solving machine learning problems such as anomaly detection, churn prediction, and pattern recognition using Spark.
Tomasz has also authored the Practical Data Analysis Cookbook published by Packt Publishing in 2016.
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