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

Data engineering tools

To build data pipelines, data engineers need to choose the right tools for the job. Data engineering is part of the overall big data ecosystem and has to account for the three Vs of big data:

  • Volume: The volume of data has grown substantially. Moving a thousand records from a database requires different tools and techniques than moving millions of rows or handling millions of transactions a minute.
  • Variety: Data engineers need tools that handle a variety of data formats in different locations (databases, APIs, files).
  • Velocity: The velocity of data is always increasing. Tracking the activity of millions of users on a social network or the purchases of users all over the world requires data engineers to operate often in near real time.

Programming languages

The lingua franca of data engineering is SQL. Whether you use low-code tools or a specific programming language, there is almost no way to get around knowing SQL. A strong foundation...