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 the data pipeline

This data pipeline will be slightly different from the previous pipelines in that we will need to use a trick to start it off. We will have two paths to the same database – one of which we will turn off once it has run the first time, and we will have a processor that connects to itself for the success relationship. The following screenshot shows the completed pipeline:

Figure 6.1 – The complete pipeline

The preceding screenshot may look complicated, but I assure you that it will make sense by the end of this chapter.

Mapping a data type

Before you can build the pipeline, you need to map a field in Elasticsearch so that you get the benefit of the coordinates by mapping them as the geopoint data type. To do that, open Kibana at http://localhost:5601. At the toolbar, select Dev Tools (the wrench icon) and enter the code shown in the left panel of the following screenshot, and then click the run arrow. If it was successful...