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Data Engineering with Python
By :
Data Engineering with Python
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Overview of this book
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.
Table of Contents (21 chapters)
Preface
Section 1: Building Data Pipelines – Extract Transform, and Load
Chapter 1: What is Data Engineering?
Chapter 2: Building Our Data Engineering Infrastructure
Chapter 3: Reading and Writing Files
Chapter 4: Working with Databases
Chapter 5: Cleaning, Transforming, and Enriching Data
Chapter 6: Building a 311 Data Pipeline
Section 2:Deploying Data Pipelines in Production
Chapter 7: Features of a Production Pipeline
Chapter 8: Version Control with the NiFi Registry
Chapter 9: Monitoring Data Pipelines
Chapter 10: Deploying Data Pipelines
Chapter 11: Building a Production Data Pipeline
Section 3:Beyond Batch – Building Real-Time Data Pipelines
Chapter 12: Building a Kafka Cluster
Chapter 13: Streaming Data with Apache Kafka
Chapter 14: Data Processing with Apache Spark
Chapter 15: Real-Time Edge Data with MiNiFi, Kafka, and Spark
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