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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

Technical requirements

The commands inside the recipes of this chapter use Linux syntax. If you don’t use a Linux-based system, you may need to adapt the commands:

  • Docker or Docker Desktop
  • The SQL client of your choice (recommended); we recommend DBeaver, since it has a community-free version

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

Note

Windows users might get an error message such as Docker Desktop requires a newer WSL kernel version. This can be fixed by following the steps here: https://docs.docker.com/desktop/windows/wsl/.

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