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Key benefits

Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples

Design data models and learn how to extract, transform, and load (ETL) data using Python

Schedule, automate, and monitor complex data pipelines in production

Description

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.

Who is this book for?

This book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required.

What you will learn

Understand how data engineering supports data science workflows

Discover how to extract data from files and databases and then clean, transform, and enrich it

Configure processors for handling different file formats as well as both relational and NoSQL databases

Find out how to implement a data pipeline and dashboard to visualize results

Use staging and validation to check data before landing in the warehouse

Build real-time pipelines with staging areas that perform validation and handle failures

Get to grips with deploying pipelines in the production environment

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

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Christoph Oct 03, 2021

This book does a good job introducing the use of NiFi and Airflow as data orchestrators for data pipelines as well as briefly mentioning Kafka and Spark.NiFiNiFi is explained in detail in this book. It covers subjects that go beyond the basics and are not easily googled. Since NiFi tutorials are rather rare compared to many other technologies, this is a valuable resource for learning it. It covers topics such as deploying to production, versioning and monitoring.Online you find some false information that versioning and automated deployment wouldn't be possible with NiFi but this book shows how it's done!AirflowIn this book Airflow is also introduced and you're shown how to setup DAGs with it. If you're mainly interested in Airflow there are better books for you, e.g. "Data Pipelines With Apache Airflow". Nevertheless this book will teach you how to get started with Airflow as well and how to setup simple project sized data pipelines from extraction to visualization.Real-time processingAt the end of the book Kafka and Spark are introduced as well. The content on these is not comprehensive. You will definitely need another resource to learn more on those in case that's what you're intetested in.What I didn't likeThis book shows you how to install the tools mentioned by hand. I would have much preferred it if a Docker setup was used. Others pointed out that they had problems installing tools and copying examples 1:1. I didn't have such problems since I installed everything using Docker and since it often is a relevant skill in Data Engineering it would not have been out of place to use it here as well.The title can be criticized as well since it's broader than the content. Something like "Data Pipelines with NiFi and Airflow" might have been more fitting.SummaryI'm giving this book 5 stars since it's hard to find a good resource on NiFi and this book is it. After you read it, you will see that both Airflow and NiFi are good orchestration tools for very similar use cases.

Amazon Verified review

Amir Esmaeili Nov 15, 2023

Einfach Super!

Amazon Verified review

jml Dec 20, 2020

Data Engineering With Python provides a solid overview of pipelining and database connections for those tasked with processing both batch and stream data flows. Not only for the data miners, this book will be useful as well in a CI/CD environment using Kafka and Spark. It’s very readable and contains lots of practical, illustrative examples.Hits — solid explanations and demonstrations of Pandas, Zookeeper, Kafka, and Spark. Also introduces Great Expectations, NiFi, Airflow, and Faker, all of which are tied together in a usable demonstration environment. Pipeline implementation’s thoroughly covered as well.Misses — the book could use a little fine-tuning as to Python 3; some of the instructions are rather downrev, and concepts like 311 / SeeClickFix are sort of dropped in without a lot of explanation. Also, there’s a heavy focus on SQL and almost no coverage of noSQL databases.Overall, a good addition to the bookshelf if you’re using any of these Python packages. Readable and useful for anyone supporting data analysis.

Amazon Verified review

Michael Porter Dec 17, 2020

This is a very hands on guide to building data pipelines with Python and a number of other tools that would be very useful for anyone looking to handle large data sets, clean or enhance data. Paul does a great job of breaking down the difference between a Data Scientist and a Data Engineer while also covering areas of overlap.His break down of the tools that are available and a number of quick start style tutorials would be useful to anyone who's experienced enough to understand them. This book is clearly not for beginners but is clear and concise enough to ensure that's it's not only for experts either.My only real compliant is that the book skips over a whole category of NoSQL databases, namely Graph Databases i.e. Neo4j, Neptune, etc. which are a fast growing part of data science as a whole.All in all a very through introduction to data engineering that focuses mostly on SQL and would help any experienced Python developer get their toes wet in the field.

Amazon Verified review

Habib Hezarehee Nov 30, 2020

The book is a little bit (not so remarkable) damaged. But since I love the content so I stay away from returning :p

Amazon Verified review

Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python

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