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Data Engineering with Python

You're reading from  Data Engineering with Python

Product type Book
Published in Oct 2020
Publisher Packt
ISBN-13 9781839214189
Pages 356 pages
Edition 1st Edition
Languages
Author (1):
Paul Crickard Paul Crickard
Profile icon Paul Crickard

Table of Contents (21) Chapters

Preface 1. Section 1: Building Data Pipelines – Extract Transform, and Load
2. Chapter 1: What is Data Engineering? 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

Using the NiFi variable registry

When you are building your data pipelines, you are hardcoding variables—with the exception of some expression language where you extract data from the flowfile. When you move the data pipeline to production, you will need to change the variables in your data pipeline, and this can be time consuming and error prone. For example, you will have a different test database than production. When you deploy your data pipeline to production, you need to point to production and change the processor. Or you can use the variable registry.

Using the postgresToelasticsearch processor group from Chapter 4, Working with Databases, I will modify the data pipeline to use the NiFi variable registry. As a reminder, the data pipeline is shown in the following screenshot:

Figure 10.8 – A data pipeline to query PostgreSQL and save the results to Elasticsearch

From outside the processor group, right-click on it and select Variables...

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