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

Handling databases with NiFi processors

In the previous sections, you learned how to read and write CSV and JSON files using Python. Reading files is such a common task that tools such as NiFi have prebuilt processors to handle it. In this section, you will build the same data pipeline as in the previous section. In NiFi, the data pipeline will look as shown in the following screenshot:

Figure 4.9 – A NiFi data pipeline to move data from PostgreSQL to Elasticsearch

The data pipeline contains one more task than the Airflow version, but otherwise, it should look straightforward. The following sections will walk you through building the data pipeline.

Extracting data from PostgreSQL

The processor most used for handling relational databases in NiFi is the ExecuteSQLRecord processor. Drag the Processor icon to the canvas, and search for the ExecuteSQLRecord processor. Once it has been added to the canvas, you need to configure it.

Configuring the...