Book Image

Data Ingestion with Python Cookbook

By : Gláucia Esppenchutz
Book Image

Data Ingestion with Python Cookbook

By: Gláucia Esppenchutz

Overview of this book

Data Ingestion with Python Cookbook offers a practical approach to designing and implementing data ingestion pipelines. It presents real-world examples with the most widely recognized open source tools on the market to answer commonly asked questions and overcome challenges. You’ll be introduced to designing and working with or without data schemas, as well as creating monitored pipelines with Airflow and data observability principles, all while following industry best practices. The book also addresses challenges associated with reading different data sources and data formats. As you progress through the book, you’ll gain a broader understanding of error logging best practices, troubleshooting techniques, data orchestration, monitoring, and storing logs for further consultation. By the end of the book, you’ll have a fully automated set that enables you to start ingesting and monitoring your data pipeline effortlessly, facilitating seamless integration with subsequent stages of the ETL process.
Table of Contents (17 chapters)
1
Part 1: Fundamentals of Data Ingestion
9
Part 2: Structuring the Ingestion Pipeline

Technical requirements

You can find the code for this chapter in the GitHub repository here: https://github.com/PacktPublishing/Data-Ingestion-with-Python-Cookbook.

Installing and running Airflow

This chapter requires that Airflow is installed on your local machine. You can install it directly on your operating system (OS) or by using a Docker image. For more information, refer to the Configuring Docker for Airflow recipe in Chapter 1.

After following the steps described in Chapter 1, ensure your Airflow runs correctly. You can do that by checking the Airflow UI here: http://localhost:8080.

If you are using a Docker container (as I am) to host your Airflow application, you can check its status on the terminal by running the following command:

$ docker ps

You can see the command running here:

Figure 10.1 – Airflow containers running

Figure 10.1 – Airflow containers running

For Docker, check the container status on Docker Desktop, as shown in the following screenshot:

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