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)
1
Section 1: Building Data Pipelines – Extract Transform, and Load
8
Section 2:Deploying Data Pipelines in Production
14
Section 3:Beyond Batch – Building Real-Time Data Pipelines

Building a Kibana dashboard

Now that your SeeClickFix data pipeline has loaded data in Elasticsearch, it would be nice to see the results of the data, as would an analyst. Using Kibana, you can do just that. In this section, you will build a Kibana dashboard for your data pipeline.

To open Kibana, browse to http://localhost:5601 and you will see the main window. At the bottom of the toolbar (on the left of the screen; you may need to expand it), click the management icon at the bottom. You need to select Create new Index Pattern and enter scf*, as shown in the following screenshot:

Figure 6.4 – Creating the index pattern in Kibana

Figure 6.4 – Creating the index pattern in Kibana

When you click the next step, you will be asked to select a Time Filter field name. Because there are several fields with times in them, and they are in a format that is already recognizable by Elasticsearch, they will be indexed as such, and you can select a primary time filter. The field selected will be the default...