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Data Ingestion with Python Cookbook

Data Ingestion with Python Cookbook

By : Gláucia Esppenchutz
4.5 (4)
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Data Ingestion with Python Cookbook

Data Ingestion with Python Cookbook

4.5 (4)
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)
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1
Part 1: Fundamentals of Data Ingestion
9
Part 2: Structuring the Ingestion Pipeline

Reading a JSON file

JavaScript Object Notation (JSON) is a semi-structured data format. Some articles also define JSON as an unstructured data format, but the truth is this format can be used for multiple purposes.

JSON structure uses nested objects and arrays and, due to its flexibility, many applications and APIs use it to export or share data. That is why describing this file format in this chapter is essential.

This recipe will explore how to read a JSON file using a built-in Python library and explain how the process works.

Note

JSON is an alternative to XML files, which are very verbose and require more coding to manipulate their data.

Getting ready

This recipe is going to use the GitHub Events JSON data, which can be found in the GitHub repository of this book at https://github.com/jdorfman/awesome-json-datasets with other free JSON data.

To retrieve the data, click on GitHub API | Events, copy the content from the page, and save it as a .json file...

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