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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

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781839214189
Length 356 pages
Edition 1st Edition
Languages
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Author (1):
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Paul Crickard Paul Crickard
Author Profile Icon Paul Crickard
Paul Crickard
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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

Creating ZooKeeper and Kafka clusters

Most tutorials on running applications that can be distributed often only show how to run a single node and then you are left wondering how you would run this in production. In this section, you will build a three-node ZooKeeper and Kafka cluster. It will run on a single machine. However, I will split each instance into its own folder and each folder simulates a server. The only modification when running on different servers would be to change localhost to the server IP.

The next chapter will go into detail on the topic of Apache Kafka, but for now it is enough to understand that Kafka is a tool for building real-time data streams. Kafka was developed at LinkedIn and is now an Apache project. You can find Kafka on the web at http://kafka.apache.org. The website is shown in the following screenshot:

Figure 12.1 – Apache Kafka website

Kafka requires another application, ZooKeeper, to manage information about the...

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