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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

Summary

In this chapter, you learned how to build a data pipeline using data from a REST API. You also added a flow to the data pipeline to allow you to backfill the data, or to recreate a database with all of the data using a single pipeline.

The second half of the chapter provided a basic overview of how to build a dashboard using Kibana. Dashboards will usually be outside the responsibilities of a data engineer. In smaller firms, however, this could very well be your job. Furthermore, being able to quickly build a dashboard can help validate your data pipeline and look for any possible errors in the data.

In the next chapter, we begin a new section of this book, where you will take the skills you have learned and improve them by making your pipelines ready for production. You will learn about deployment, better validation techniques, and other skills needed when you are running pipelines in a production environment.

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