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

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.