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

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 use 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 instance runs correctly. You can do that by checking the Airflow UI at http://localhost:8080.

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

$ docker ps

Here is the status of the container:

Figure 11.1 –  Airflow containers running

Figure 11.1 – Airflow containers running

Or you can check the container status on Docker Desktop:

Figure 11.2 – Docker Desktop showing Airflow running containers

Figure 11.2 – Docker Desktop showing Airflow running containers