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

Using the Jupyter Notebook is not mandatory but allows us to explore the code interactively. Since we will execute both Python and PySpark code, Jupyter can help us to understand the scripts better. Once you have Jupyter installed, you can execute it using the following line:

$ jupyter notebook

It is recommended to create a separate folder to store the Python files or notebooks we will cover in this chapter; however, feel free to organize it in the most appropriate way for you.