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- Data Engineering with Python. Work with massive datasets to design data models and automate data pipelines using Python
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Data Engineering with Python. Work with massive datasets to design data models and automate data pipelines using Python
Ebook
Data engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python.
The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You’ll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You’ll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you’ll build architectures on which you’ll learn how to deploy data pipelines.
By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.
The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You’ll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You’ll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you’ll build architectures on which you’ll learn how to deploy data pipelines.
By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.
- 1. What is Data Engineering?
- 2. Building Our Data Engineering Infrastructure
- 3. Reading and Writing Files
- 4. Working with Databases
- 5. Cleaning, Transforming, and Enriching Data
- 6. Building a 311 Data Pipeline
- 7. Features of a Production Pipeline
- 8. Version Control Using the NiFi Registry
- 9. Monitoring and Logging Pipelines
- 10. Deploying Your Pipelines
- 11. Building a Production Data Pipeline
- 12. Building a Kafka Cluster
- 13. Streaming Data with Apache Kafka
- 14. Data Processing with Apache Spark
- 15. Real-Time Edge Data with MiNiFi, Kafka, and Spark
- 16. Appendix
- Title: Data Engineering with Python. Work with massive datasets to design data models and automate data pipelines using Python
- Author: Paul Crickard
- Original title: Data Engineering with Python. Work with massive datasets to design data models and automate data pipelines using Python
- ISBN: 9781839212307, 9781839212307
- Date of issue: 2020-10-23
- Format: Ebook
- Item ID: e_2agm
- Publisher: Packt Publishing