You're reading from Data Engineering with Python

Product type Book

Published in Oct 2020

Publisher Packt

ISBN-13 9781839214189

Pages 356 pages

Edition 1st Edition

Languages

Python

Concepts

Data Analysis

Author (1):

Paul Crickard

Table of Contents (21) Chapters

Preface

1. Section 1: Building Data Pipelines – Extract Transform, and Load

2. Chapter 1: What is Data Engineering?

3. Chapter 2: Building Our Data Engineering Infrastructure

4. Chapter 3: Reading and Writing Files

5. Chapter 4: Working with Databases

6. Chapter 5: Cleaning, Transforming, and Enriching Data

7. Chapter 6: Building a 311 Data Pipeline

8. Section 2:Deploying Data Pipelines in Production

9. Chapter 7: Features of a Production Pipeline

10. Chapter 8: Version Control with the NiFi Registry

11. Chapter 9: Monitoring Data Pipelines

12. Chapter 10: Deploying Data Pipelines

13. Chapter 11: Building a Production Data Pipeline

14. Section 3:Beyond Batch – Building Real-Time Data Pipelines

15. Chapter 12: Building a Kafka Cluster

16. Chapter 13: Streaming Data with Apache Kafka

17. Chapter 14: Data Processing with Apache Spark

18. Chapter 15: Real-Time Edge Data with MiNiFi, Kafka, and Spark

19. Other Books You May Enjoy

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Appendix

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

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...