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

Processing data with PySpark

Before processing data with PySpark, let's run one of the samples to show how Spark works. Then, we will skip the boilerplate in later examples and focus on data processing. The Jupyter notebook for the Pi Estimation example from the Spark website at is shown in the following screenshot:

Figure 14.6 – The Pi Estimation example in a Jupyter notebook

The example from the website will not run without some modifications. In the following points, I will walk through the cells:

  1. The first cell imports findspark and runs the init() method. This was explained in the preceding section as the preferred method to include PySpark in Jupyter notebooks. The code is as follows:
    import findspark
  2. The next cell imports the pyspark library and SparkSession. It then creates the session by passing the head node of the Spark cluster. You can get the URL from the Spark web UI...