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Data Engineering with Python

Data Engineering with Python

By : Paul Crickard
2.6 (24)
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Data Engineering with Python

Data Engineering with Python

2.6 (24)
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)
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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

Monitoring NiFi with processors

Instead of relying on watching the NiFi GUI, you can insert a processor into your data pipeline to report what is happening with the pipeline. For example, you can use the PutSlack processor to send messages on failures or success.

To send Slack messages, you will need to create an app in your Slack workspace. You can do this by browsing to https://api.slack.com/apps. Click Create New App, as shown in the following screenshot:

Figure 9.14 – Creating a new app

Slack will ask you to name your app and then select a workspace, as shown in the following screenshot:

Figure 9.15 – Specifying a name and workspace for your app

When finished, you will be redirected to the app page. Under the Features heading, click Incoming Webhooks and turn it on, as shown in the following screenshot:

Figure 9.16 – Activating Incoming Webhooks

You will be asked to select a channel...

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Data Engineering with Python
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