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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

Chapter 2: Building Our Data Engineering Infrastructure

In the previous chapter, you learned what data engineers do and their roles and responsibilities. You were also introduced to some of the tools that they use, primarily the different types of databases, programming languages, and data pipeline creation and scheduling tools.

In this chapter, you will install and configure several tools that will help you throughout the rest of this book. You will learn how to install and configure two different databases – PostgreSQL and Elasticsearch – two tools to assist in building workflows – Airflow and Apache NiFi, and two administrative tools – pgAdmin for PostgreSQL and Kibana for Elasticsearch.

With these tools, you will be able to write data engineering pipelines to move data from one source to another and also be able to visualize the results. As you learn how to build pipelines, being able to see the data and how it has transformed will be useful to...

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