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

Using the NiFi variable registry

When you are building your data pipelines, you are hardcoding variables—with the exception of some expression language where you extract data from the flowfile. When you move the data pipeline to production, you will need to change the variables in your data pipeline, and this can be time consuming and error prone. For example, you will have a different test database than production. When you deploy your data pipeline to production, you need to point to production and change the processor. Or you can use the variable registry.

Using the postgresToelasticsearch processor group from Chapter 4, Working with Databases, I will modify the data pipeline to use the NiFi variable registry. As a reminder, the data pipeline is shown in the following screenshot:

Figure 10.8 – A data pipeline to query PostgreSQL and save the results to Elasticsearch

From outside the processor group, right-click on it and select Variables...