Reader small image

You're reading from  Data Engineering with Python

Product typeBook
Published inOct 2020
Reading LevelBeginner
PublisherPackt
ISBN-139781839214189
Edition1st Edition
Languages
Right arrow
Author (1)
Paul Crickard
Paul Crickard
author image
Paul Crickard

Paul Crickard authored a book on the Leaflet JavaScript module. He has been programming for over 15 years and has focused on GIS and geospatial programming for 7 years. He spent 3 years working as a planner at an architecture firm, where he combined GIS with Building Information Modeling (BIM) and CAD. Currently, he is the CIO at the 2nd Judicial District Attorney's Office in New Mexico.
Read more about Paul Crickard

Right arrow

Summary

In this chapter, you learned three key features of production data pipelines: staging and validation, idempotency, and atomicity. You learned how to use Great Expectations to add production-grade validation to your data pipeline staged data. You also learned how you could incorporate idempotency and atomicity into your pipelines. With these skills, you can build more robust, production-ready pipelines.

In the next chapter, you will learn how to use version control with the NiFi registry.

lock icon
The rest of the page is locked
Previous PageNext Chapter
You have been reading a chapter from
Data Engineering with Python
Published in: Oct 2020Publisher: PacktISBN-13: 9781839214189

Author (1)

author image
Paul Crickard

Paul Crickard authored a book on the Leaflet JavaScript module. He has been programming for over 15 years and has focused on GIS and geospatial programming for 7 years. He spent 3 years working as a planner at an architecture firm, where he combined GIS with Building Information Modeling (BIM) and CAD. Currently, he is the CIO at the 2nd Judicial District Attorney's Office in New Mexico.
Read more about Paul Crickard