Learn effective, time-saving techniques on leveraging the power of Python and using it in the Spark ecosystem. Build and deploy data-intensive applications at scale using Python and Apache Spark.
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Building and deploying data-intensive applications at scale using Python and Apache Spark.
Apache Spark is an open-source distributed engine for querying and processing data. In this tutorial, we provide a brief overview of Spark and its stack. This tutorial presents effective, time-saving techniques on 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 Apache Spark architecture and how to set up a Python environment for Spark.
You'll learn about different techniques for collecting data, and distinguish between (and understand) techniques for processing data. Next, we provide an in-depth review of RDDs and contrast them with DataFrames. We provide examples of how to read data from files and from HDFS and how to specify schemas using reflection or programmatically (in the case of DataFrames). The concept of lazy execution is described and we outline various transformations and actions specific to RDDs and DataFrames.
Finally, we show you how to use SQL to interact with DataFrames. By the end of this tutorial, you will have learned how to process data using Spark DataFrames and mastered data collection techniques by distributed data processing.
About the Author
- Tomasz Drabas is a Data Scientist working for Microsoft and currently residing in the Seattle area. He has over 12 years' international experience in data analytics and data science in numerous fields: advanced technology, airlines, telecommunications, finance, and consulting.
- Tomasz started his career in 2003 with LOT Polish Airlines in Warsaw, Poland while finishing his Master's degree in strategy management. In 2007, he moved to Sydney to pursue a doctoral degree in operations research at the University of New South Wales, School of Aviation; his research crossed boundaries between discrete choice modeling and airline operations research. During his time in Sydney, he worked as a Data Analyst for Beyond Analysis Australia and as a Senior Data Analyst/Data Scientist for Vodafone Hutchison Australia among others. He has also published scientific papers, attended international conferences, and served as a reviewer for scientific journals.
- In 2015 he relocated to Seattle to begin his work for Microsoft. While there, he has worked on numerous projects involving solving problems in high-dimensional feature space.
- Here is his LinkedIn Profile: https://www.linkedin.com/in/tomekd/
- A firm understanding of Python
- Learn about Apache Spark and the Spark 2.0 architecture
- Understand schemas for RDD, lazy executions, and transformations
- Explore the sorting and saving elements of RDD
- Build and interact with Spark DataFrames using Spark SQL
- Create and explore various APIs to work with Spark DataFrames
- Learn how to change the schema of a DataFrame programmatically
- Explore how to aggregate, transform, and sort data with DataFrames
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