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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

Building a distributed data pipeline

Building a distributed data pipeline is almost exactly the same as building a data pipeline to run on a single machine. NiFi will handle the logistics of passing and recombining the data. A basic data pipeline is shown in the following screenshot:

Figure 16.4 – A basic data pipeline to generate data, extract attributes to json, and write to disk

The preceding data pipeline uses the GenerateFlowFile processor to create unique flowfiles. This is passed downstream to the AttributesToJSON processor, which extracts the attributes and writes to the flowfile content. Lastly, the file is written to disk at /home/paulcrickard/output.

Before running the data pipeline, you will need to make sure that you have the output directory for the PutFile processor on each node. Earlier, I said that data pipelines are no different when distributed, but there are some things you must keep in mind, one being that PutFile will write...

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