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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

Summary

In this chapter, you learned what data engineering is. Data engineering roles and responsibilities vary depending on the maturity of an organization's data infrastructure. But data engineering, at its simplest, is the creation of pipelines to move data from one source or format to another. This may or may not involve data transformations, processing engines, and the maintenance of infrastructure.

Data engineers use a variety of programming languages, but most commonly Python, Java, or Scala, as well as proprietary and open source transactional databases and data warehouses, both on-premises and in the cloud, or a mixture. Data engineers need to be knowledgeable in many areas – programming, operations, data modeling, databases, and operating systems. The breadth of the field is part of what makes it fun, exciting, and challenging. To those willing to accept the challenge, data engineering is a rewarding career.

In the next chapter, we will begin by setting up an environment to start building data pipelines.

You have been reading a chapter from
Data Engineering with Python
Published in: Oct 2020
Publisher: Packt
ISBN-13: 9781839214189
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