Search icon
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
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

Summary

In this chapter, you learned how to use Python to query and insert data into both relational and NoSQL databases. You also learned how to use both Airflow and NiFi to create data pipelines. Database skills are some of the most important for a data engineer. There will be very few data pipelines that do not touch on them in some way. The skills you learned in this chapter provide the foundation for the other skills you will need to learn – primarily SQL. Combining strong SQL skills with the data pipeline skills you learned in this chapter will allow you to accomplish most of the data engineering tasks you will encounter.

In the examples, the data pipelines were not idempotent. Every time they ran, you got new results, and results you did not want. We will fix that in Section 2, Deploying Pipelines into Production. But before you get to that, you will need to learn how to handle common data issues, and how to enrich and transform your data.

The next chapter will...

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 $15.99/month. Cancel anytime}