Preface

Data engineering provides the foundation for data science and analytics and constitutes an important aspect of all businesses. This book will help you to explore various tools and methods that are used to understand 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 this book is 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 this book covers

Chapter 1, What Is Data Engineering, defines data engineering. It will introduce you to the skills, roles, and responsibilities of a data engineer. You will also learn how data engineering fits in with other disciplines, such as data science.

Chapter 2, Building Our Data Engineering Infrastructure, explains how to install and configure the tools used throughout this book. You will install two databases – ElasticSearch and PostgreSQL – as well as NiFi, Kibana, and, of course, Python.

Chapter 3, Reading and Writing Files, provides an introduction to reading and writing files in Python as well as data pipelines in NiFi. It will focus on Comma Seperated Values (CSV) and JavaScript Object Notation (JSON) files.

Chapter 4, Working with Databases, explains the basics of working with SQL and NoSQL databases. You will query both types of databases and view the results in Python and through the use of NiFi. You will also learn how to read a file and insert it into the databases.

Chapter 5, Cleaning and Transforming Data, explains how to take the files or database queries and perform basic exploratory data analysis. This analysis will allow you to view common data problems. You will then use Python and NiFi to clean and transform the data with a view to solving those common data problems.

Chapter 6, Project – Building a 311 Data Pipeline, sets out a project in which you will build a complete data pipeline. You will learn how to read from an API and use all of the skills acquired from previous chapters. You will clean and transform the data as well as enrich it with additional data. Lastly, you will insert the data into a warehouse and build a dashboard to visualize it.

Chapter 7, Features of a Production Data Pipeline, covers what is needed in a data pipeline to make it ready for production. You will learn about atomic transactions and how to make data pipelines idempotent.

Chapter 8, Version Control Using the NiFi Registry, explains how to version control your data pipelines. You will install and configure the NiFi registry. You will also learn how to configure the registry to use GitHub as the source of your NiFi processors.

Chapter 9, Monitoring and Logging Data Pipelines, teaches you the basics of monitoring and logging data pipelines. You will learn about the features of the NiFi GUI for monitoring. You will also learn how to use NiFi processors to log and monitor performance from within your data pipelines. Lastly, you will learn the basics of the NiFi API.

Chapter 10, Deploying Your Data Pipelines, proposes a method for building test and production environments for NiFi. You will learn how to move your completed and version-controlled data pipelines into a production environment.

Chapter 11, Project – Building a Production Data Pipeline, explains how to build a production data pipeline. You will use the project from Chapter 6 and add a number of features. You will version control the data pipeline as well as adding monitoring and logging features.

Chapter 12, Building an Apache Kafka Cluster, explains how to install and configure a three-node Apache Kafka cluster. You will learn the basics of Kafka – streams, topics, and consumers.

Chapter 13, Streaming Data with Kafka, explains how, using Python, you can write to Kafka topics and how to consume that data. You will write Python code for both consumers and producers using a third-party Python library.

Chapter 14, Data Processing with Apache Spark, walks you through the installation and configuration of a three-node Apache Spark cluster. You will learn how to use Python to manipulate data in Spark. This will be reminiscent of working with pandas DataFrames from Section 1 of this book.

Chapter 15, Project – Real-Time Edge Data – Kafka, Spark, and MiNiFi, introduces MiNiFi, which is a separate project to make NiFi available on low-resource devices such as Internet of Things devices. You will build a data pipeline that sends data from MiNiFi to your NiFi instance.

The Appendix teaches you the basics of clustering with Apache NiFi. You will learn how to distribute data pipelines and some caveats in doing so. You will also learn how to allow data pipelines to run on a single, specified node and not run distributed while in a cluster.

To get the most out of this book

You should have a basic understanding of Python. You will not be required to know any existing libraries, just a fundamental understanding of variables, functions, and how to run a program. You should also know the basics of Linux. If you can run a command in the terminal and open new terminal windows, that should be sufficient.

If you are using the digital version of this book, we advise you to type the code yourself or access the code via the GitHub repository (link available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Download the example code files

You can download the example code files for this book at https://github.com/PacktPublishing/Data-Engineering-with-Python. In case there’s an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781839214189_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Next, pass the arguments dictionary to DAG().”

A block of code is set as follows:

import datetime as dt from datetime import timedelta
from airflow import DAG from airflow.operators.bash_operator import BashOperator from airflow.operators.python_operator import PythonOperator
import pandas as pd

Any command-line input or output is written as follows:

# web properties #
nifi.web.http.port=9300

Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: “Click on DAG and select Tree View.”

Tips or important notes

Appear like this.

Get in touch

Feedback from our readers is always welcome.

General feedback: If you have questions about any aspect of this book, mention the book title in the subject of your message and email us at [e-mail].

Errata: Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you have found a mistake in this book, we would be grateful if you would report this to us. Please visit www.packtpub.com/support/errata, selecting your book, clicking on the Errata Submission Form link, and entering the details.

Piracy: If you come across any illegal copies of our works in any form on the internet, we would be grateful if you would provide us with the location address or website name. Please contact us at [e-mail] with a link to the material.

If you are interested in becoming an author: If there is a topic that you have expertise in, and you are interested in either writing or contributing to a book, please visit authors.packtpub.com.

Reviews

Please leave a review. Once you have read and used this book, why not leave a review on the site that you purchased it from? Potential readers can then see and use your unbiased opinion to make purchase decisions, we at Packt can understand what you think about our products, and our authors can see your feedback on their book. Thank you!

For more information about Packt, please visit packt.com.