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

Building a distributed data pipeline

Building a distributed data pipeline is almost exactly the same as building a data pipeline to run on a single machine. NiFi will handle the logistics of passing and recombining the data. A basic data pipeline is shown in the following screenshot:

Figure 16.4 – A basic data pipeline to generate data, extract attributes to json, and write to disk

The preceding data pipeline uses the GenerateFlowFile processor to create unique flowfiles. This is passed downstream to the AttributesToJSON processor, which extracts the attributes and writes to the flowfile content. Lastly, the file is written to disk at /home/paulcrickard/output.

Before running the data pipeline, you will need to make sure that you have the output directory for the PutFile processor on each node. Earlier, I said that data pipelines are no different when distributed, but there are some things you must keep in mind, one being that PutFile will write...