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

Chapter 2: Building Our Data Engineering Infrastructure

In the previous chapter, you learned what data engineers do and their roles and responsibilities. You were also introduced to some of the tools that they use, primarily the different types of databases, programming languages, and data pipeline creation and scheduling tools.

In this chapter, you will install and configure several tools that will help you throughout the rest of this book. You will learn how to install and configure two different databases – PostgreSQL and Elasticsearch – two tools to assist in building workflows – Airflow and Apache NiFi, and two administrative tools – pgAdmin for PostgreSQL and Kibana for Elasticsearch.

With these tools, you will be able to write data engineering pipelines to move data from one source to another and also be able to visualize the results. As you learn how to build pipelines, being able to see the data and how it has transformed will be useful to...