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

Installing and running Spark

Apache Spark is a distributed data processing engine that can handle both streams and batch data, and even graphs. It has a core set of components and other libraries that are used to add functionality. A common depiction of the Spark ecosystem is shown in the following diagram:

Figure 14.1 – The Apache Spark ecosystem

To run Spark as a cluster, you have several options. Spark can run in a standalone mode, which uses a simple cluster manager provided by Spark. It can also run on an Amazon EC2 instance, using YARN, Mesos, or Kubernetes. In a production environment with a significant workload, you would probably not want to run in standalone mode; however, this is how we will stand up our cluster in this chapter. The principles will be the same, but the standalone cluster provides the fastest way to get you up and running without needing to dive into more complicated infrastructure.

To install Apache Spark, take the following...