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Data Engineering with Python

You're reading from   Data Engineering with Python Work with massive datasets to design data models and automate data pipelines using Python

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781839214189
Length 356 pages
Edition 1st Edition
Languages
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Author (1):
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Paul Crickard Paul Crickard
Author Profile Icon Paul Crickard
Paul Crickard
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Table of Contents (21) Chapters Close

Preface 1. Section 1: Building Data Pipelines – Extract Transform, and Load
2. Chapter 1: What is Data Engineering? FREE CHAPTER 3. Chapter 2: Building Our Data Engineering Infrastructure 4. Chapter 3: Reading and Writing Files 5. Chapter 4: Working with Databases 6. Chapter 5: Cleaning, Transforming, and Enriching Data 7. Chapter 6: Building a 311 Data Pipeline 8. Section 2:Deploying Data Pipelines in Production
9. Chapter 7: Features of a Production Pipeline 10. Chapter 8: Version Control with the NiFi Registry 11. Chapter 9: Monitoring Data Pipelines 12. Chapter 10: Deploying Data Pipelines 13. Chapter 11: Building a Production Data Pipeline 14. Section 3:Beyond Batch – Building Real-Time Data Pipelines
15. Chapter 12: Building a Kafka Cluster 16. Chapter 13: Streaming Data with Apache Kafka 17. Chapter 14: Data Processing with Apache Spark 18. Chapter 15: Real-Time Edge Data with MiNiFi, Kafka, and Spark 19. Other Books You May Enjoy Appendix

Processing data with PySpark

Before processing data with PySpark, let's run one of the samples to show how Spark works. Then, we will skip the boilerplate in later examples and focus on data processing. The Jupyter notebook for the Pi Estimation example from the Spark website at http://spark.apache.org/examples.html is shown in the following screenshot:

Figure 14.6 – The Pi Estimation example in a Jupyter notebook

The example from the website will not run without some modifications. In the following points, I will walk through the cells:

  1. The first cell imports findspark and runs the init() method. This was explained in the preceding section as the preferred method to include PySpark in Jupyter notebooks. The code is as follows:
    import findspark
    findspark.init()
  2. The next cell imports the pyspark library and SparkSession. It then creates the session by passing the head node of the Spark cluster. You can get the URL from the Spark web UI...
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