Book Image

Data Engineering with Python

By : Paul Crickard
Book Image

Data Engineering with Python

By: Paul Crickard

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)
Section 1: Building Data Pipelines – Extract Transform, and Load
Section 2:Deploying Data Pipelines in Production
Section 3:Beyond Batch – Building Real-Time Data Pipelines

Producing and consuming with Python

You can create producers and consumers for Kafka using Python. There are multiple Kafka Python libraries – Kafka-Python, PyKafka, and Confluent Python Kafka. In this section, I will use Confluent Python Kafka, but if you want to use an open source, community-based library, you can use Kafka-Python. The principles and structure of the Python programs will be the same no matter which library you choose.

To install the library, you can use pip. The following command will install it:

pip3 install confluent-kafka

Once the library has finished installing, you can use it by importing it into your applications. The following sections will walk through writing a producer and consumer.

Writing a Kafka producer in Python

To write a producer in Python, you will create a producer, send data, and listen for acknowledgements. In the previous examples, you used Faker to create fake data about people. You will use it again to generate the data...