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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 Section 1: Building Data Pipelines – Extract Transform, and Load
Chapter 1: What is Data Engineering? Chapter 2: Building Our Data Engineering Infrastructure Chapter 3: Reading and Writing Files Chapter 4: Working with Databases Chapter 5: Cleaning, Transforming, and Enriching Data Chapter 6: Building a 311 Data Pipeline Section 2:Deploying Data Pipelines in Production
Chapter 7: Features of a Production Pipeline Chapter 8: Version Control with the NiFi Registry Chapter 9: Monitoring Data Pipelines Chapter 10: Deploying Data Pipelines Chapter 11: Building a Production Data Pipeline Section 3:Beyond Batch – Building Real-Time Data Pipelines
Chapter 12: Building a Kafka Cluster Chapter 13: Streaming Data with Apache Kafka Chapter 14: Data Processing with Apache Spark Chapter 15: Real-Time Edge Data with MiNiFi, Kafka, and Spark Other Books You May Enjoy Appendix

Summary

In this Appendix, you learned the basics of NiFi clustering, as well as how to build a cluster with the embedded Zookeeper and how to build distributed data pipelines. NiFi handles most of the distribution of data; you only need to keep in mind the gotchas – such as race conditions and the fact that processors need to be configured to run on any node. Using a NiFi cluster allows you to manage NiFi on several machines from a single instance. It also allows you to process large amounts of data and have some redundancy in case an instance crashes.

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