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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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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 the data pipeline

This data pipeline will be slightly different from the previous pipelines in that we will need to use a trick to start it off. We will have two paths to the same database – one of which we will turn off once it has run the first time, and we will have a processor that connects to itself for the success relationship. The following screenshot shows the completed pipeline:

Figure 6.1 – The complete pipeline

The preceding screenshot may look complicated, but I assure you that it will make sense by the end of this chapter.

Mapping a data type

Before you can build the pipeline, you need to map a field in Elasticsearch so that you get the benefit of the coordinates by mapping them as the geopoint data type. To do that, open Kibana at http://localhost:5601. At the toolbar, select Dev Tools (the wrench icon) and enter the code shown in the left panel of the following screenshot, and then click the run arrow. If it was successful...

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