Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781839214189
Length 356 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Paul Crickard Paul Crickard
Author Profile Icon Paul Crickard
Paul Crickard
Arrow right icon
View More author details
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 atomic data pipelines

The final feature of a production data pipeline that we will discuss in this chapter is atomicity. Atomicity means that if a single operation in a transaction fails, then all of the operations fail. If you are inserting 1,000 records into the database, as you did in Chapter 3, Reading and Writing Files, if one record fails, then all 1,000 fail.

In SQL databases, the database will roll back all the changes if record number 500 fails, and it will no longer attempt to continue. You are now free to retry the transaction. Failures can occur for many reasons, some of which are beyond your control. If the power or the network goes down while you are inserting records, do you want those records to be saved to the database? You would then need to determine which records in a transaction succeeded and which failed and then retry only the failed records. This would be much easier than retrying the entire transaction.

In the NiFi data pipelines you have built...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime