Apache Spark with Python - Big Data with PySpark and Spark

Video description

Learn Apache Spark and Python by 12+ hands-on examples of analyzing big data with PySpark and Spark

About This Video

  • Apache Spark gives us unlimited ability to build cutting-edge applications. It is also one of the most compelling technologies of the last decade in terms of its disruption to the big data world.
  • Spark provides in-memory cluster computing which greatly boosts the speed of iterative algorithms and interactive data mining tasks.

In Detail

This course covers all the fundamentals of Apache Spark with Python and teaches you everything you need to know about developing Spark applications using PySpark, the Python API for Spark. At the end of this course, you will gain in-depth knowledge about Apache Spark and general big data analysis and manipulations skills to help your company to adopt Apache Spark for building big data processing pipeline and data analytics applications. This course covers 10+ hands-on big data examples. You will learn valuable knowledge about how to frame data analysis problems as Spark problems. Together we will learn examples such as aggregating NASA Apache weblogs from different sources; we will explore the price trend by looking at the real estate data in California; we will write Spark applications to find out the median salary of developers in different countries through the Stack Overflow survey data; we will develop a system to analyze how maker spaces are distributed across different regions in the United Kingdom. And much much more.


Anyone who wants to fully understand how Apache Spark technology works and learn how Apache Spark is being used in the field. Software engineers who want to develop Apache Spark 2.0 applications using Spark Core and Spark SQL.Data scientists or data engineers who want to advance their career by improving their big data processing skills.

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Product information

  • Title: Apache Spark with Python - Big Data with PySpark and Spark
  • Author(s): James Lee
  • Release date: April 2018
  • Publisher(s): Packt Publishing
  • ISBN: 9781789133394