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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

Deploying a data pipeline in production

In the previous chapter, you learned how to deploy data to production, so I will not go into any great depth here, but merely provide a review. To put the new data pipeline into production, perform the following steps:

  1. Browse to your production NiFi instance. I have another instance of NiFi running on port 8080 on localhost.
  2. Drag and drop processor groups to the canvas and select Import. Choose the latest version of the processor groups you just built.
  3. Modify the variables on the processor groups to point to the database production. The table names can stay the same.

You can then run the data pipeline and you will see that the data is populated in the production database staging and warehouse tables.

The data pipeline you just built read files from a data lake, put them into a database table, ran a query to validate the table, and then inserted them into the warehouse. You could have built this data pipeline with a handful...

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