by Tomasz Drabas | Released June 2018 | ISBN: 9781789538762

Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0About This VideoLearn why and how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0.Develop and deploy efficient, scalable real-time Spark solutions.Take your understanding of using Spark with Python to the next level with this jump start guide.In DetailApache 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. Show and hide more

1. Chapter 1 : Understanding Spark
   • The Course Overview 00:01:56
   • Spark Jobs and APIs 00:05:29
2. Chapter 2 : Resilient Distributed Datasets
   • Creating RDDs 00:06:40
   • Transformations 00:06:41
   • Actions 00:04:59
3. Chapter 3 : DataFrames
   • Basic Operations with DataFrames 00:04:50
   • High End Operations – Interpolating and Querying 00:05:48
4. Chapter 4 : Prepare Data for Modeling
   • Checking for Duplicates, Missing Observations, and Outliers 00:10:44
   • Getting Familiar with Your Data 00:04:14
   • Visualization 00:02:04
5. Chapter 5 : Introducing MLlib
   • Loading and Transforming the Data 00:05:15
   • Getting to Know Your Data 00:04:39
   • Creating the Final Dataset 00:01:28
   • Predicting Infant Survival 00:03:17
6. Chapter 6 : Introducing the ML Package
   • Predicting the Chances of Infant Survival with ML 00:05:47
   • Parameter Hyper-Tuning 00:04:39
   • Other Features of PySpark ML in Action 00:08:43
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