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Data Engineering with Python

Data Engineering with Python

By : Paul Crickard
2.6 (24)
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Data Engineering with Python

Data Engineering with Python

2.6 (24)
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)
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1
Section 1: Building Data Pipelines – Extract Transform, and Load
8
Section 2:Deploying Data Pipelines in Production
14
Section 3:Beyond Batch – Building Real-Time Data Pipelines

Differentiating stream processing from batch processing

While the processing tools don't change whether you are processing streams or batches, there are two things you should keep in mind while processing streams – unbounded and time.

Data can be bounded or unbounded. Bounded data has an end, whereas unbounded data is constantly created and is possibly infinite. Bounded data is last year's sales of widgets. Unbounded data is a traffic sensor counting cars and recording their speeds on the highway.

Why is this important in building data pipelines? Because with bounded data, you will know everything about the data. You can see it all at once. You can query it, put it in a staging environment, and then run Great Expectations on it to get a sense of the ranges, values, or other metrics to use in validation as you process your data.

With unbounded data, it is streaming in and you don't know what the next piece of data will look like. This doesn't mean...

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Data Engineering with Python
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