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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

Using Python with the NiFi REST API

Using Python and the NiFi REST API, you could write your own monitoring tools, or wire up a dashboard. The NiFi REST API documentation is located at https://nifi.apache.org/docs/nifi-docs/rest-api/index.html. You can see all of the different endpoints by type and some information about each of them. This section will highlight some of the endpoints that you have covered in this chapter but by using the GUI.

The first thing we can look at are the system diagnostics. System diagnostics will show you your resource usage. You can see heap size, threads, repository usage, and several other metrics. To call the endpoint with requests, you can use the following code:

r=requests.get('http://localhost:9300/nifi-api/system-diagnostics')
data=r.json()
data['systemDiagnostics']['aggregateSnapshot']['maxHeap']
#'512 MB'
data['systemDiagnostics']['aggregateSnapshot']['totalThreads...
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