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

Building a production data pipeline

The data pipeline you build will do the following:

  • Read files from the data lake.
  • Insert the files into staging.
  • Validate the staging data.
  • Move staging to the warehouse.

The final data pipeline will look like the following screenshot:

Figure 11.3 – The final version of the data pipeline

We will build the data pipeline processor group by processor group. The first processor group will read the data lake.

Reading the data lake

In the first section of this book, you read files from NiFi and will do the same here. This processor group will consist of three processors – GetFile, EvaluateJsonPath, and UpdateCounter – and an output port. Drag the processors and port to the canvas. In the following sections, you will configure them.


The GetFile processor reads files from a folder, in this case, our data lake. If you were reading a data lake in Hadoop, you would...