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Hands-On Big Data Analytics with PySpark

You're reading from  Hands-On Big Data Analytics with PySpark

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781838644130
Pages 182 pages
Edition 1st Edition
Languages
Concepts
Authors (2):
Rudy Lai Rudy Lai
Profile icon Rudy Lai
Bartłomiej Potaczek Bartłomiej Potaczek
Profile icon Bartłomiej Potaczek
View More author details
Toc

Table of Contents (15) Chapters close

Preface 1. Installing Pyspark and Setting up Your Development Environment 2. Getting Your Big Data into the Spark Environment Using RDDs 3. Big Data Cleaning and Wrangling with Spark Notebooks 4. Aggregating and Summarizing Data into Useful Reports 5. Powerful Exploratory Data Analysis with MLlib 6. Putting Structure on Your Big Data with SparkSQL 7. Transformations and Actions 8. Immutable Design 9. Avoiding Shuffle and Reducing Operational Expenses 10. Saving Data in the Correct Format 11. Working with the Spark Key/Value API 12. Testing Apache Spark Jobs 13. Leveraging the Spark GraphX API 14. Other Books You May Enjoy

Using Spark Notebooks for quick iteration of ideas

In this section, we will answer the following questions:

  • What are Spark Notebooks?
  • How do you start Spark Notebooks?
  • How do you use Spark Notebooks?

Let's start with setting up a Jupyter Notebook-like environment for Spark. Spark Notebook is just an interactive and reactive data science environment that uses Scala and Spark.

If we view the GitHub page (https://github.com/spark-notebook/spark-notebook), we can see that what the Notebooks do is actually very straightforward, as shown in the following screenshot:

If we look at a Spark Notebook, we can see that they look very much like what Python developers use, which is Jupyter Notebooks. You have a text box allowing you to enter some code, and then you execute the code below the text box, which is similar to a Notebook format. This allows us to perform a reproducible analysis...

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