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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 a production data pipeline

The data pipeline you build will do the following:

  • Read files from the data lake.
  • Insert the files into staging.
  • Validate the staging data.
  • Move staging to the warehouse.

The final data pipeline will look like the following screenshot:

Figure 11.3 – The final version of the data pipeline

We will build the data pipeline processor group by processor group. The first processor group will read the data lake.

Reading the data lake

In the first section of this book, you read files from NiFi and will do the same here. This processor group will consist of three processors – GetFile, EvaluateJsonPath, and UpdateCounter – and an output port. Drag the processors and port to the canvas. In the following sections, you will configure them.

GetFile

The GetFile processor reads files from a folder, in this case, our data lake. If you were reading a data lake in Hadoop, you would...

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