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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 using the GUI

The NiFi GUI provides several ways to monitor your data pipelines. Using the GUI is the simplest way to start monitoring your NiFi instance.

Monitoring NiFi with the status bar

Much of the information you need is on the status bar. The status bar is below the component toolbar and looks like the following screenshot:

Figure 9.1 – Component and status toolbars

Starting at the left of the status bar, let's look at what is being monitored:

  • Active thread: This lets you know how many threads are running. You can get a sense of tasks and load.
  • Total queued data: The number of flowfiles and the combined size on disk.
  • Transmitting remote process groups and not transmitting remote process groups: You can run NiFi on multiple machines or instances on the same machine and allow process groups to communicate. These icons tell you whether they are or are not communicating.
  • Running components, stopped components...
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Data Engineering with Python
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