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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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Toc

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

Handling files using NiFi processors

In the previous sections, you learned how to read and write CSV and JSON files using Python. Reading files is such a common task that tools such as NiFi have prebuilt processors to handle it. In this section, you will learn how to handle files using NiFi processors.

Working with CSV in NiFi

Working with files in NiFi requires many more steps than you had to use when doing the same tasks in Python. There are benefits to using more steps and using Nifi, including that someone who does not know code can look at your data pipeline and understand what it is you are doing. You may even find it easier to remember what it is you were trying to do when you come back to your pipeline in the future. Also, changes to the data pipeline do not require refactoring a lot of code; rather, you can reorder processors via drag and drop.

In this section, you will create a data pipeline that reads in the data.CSV file you created in Python. It will run a query...

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