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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

Importing structured data using a well-defined schema

As seen in the previous chapter, Ingesting Data from Structured and Unstructured Databases, structured data has a standard format presented in rows and columns and is often stored inside a database.

Due to its format, the application of a DataFrame schema tends to be less complex and has several benefits, such as ensuring the ingested information is the same as the data source or follows a rule.

In this recipe, we will ingest data from a structured file such as a CSV file and apply a DataFrame schema to understand better how it is used in a real-world scenario.

Getting ready

This exercise requires the listings.csv file found inside the GitHub repository for this book. Also, make sure your SparkSession is initialized.

All the code in this recipe can be executed in Jupyter Notebook cells or a PySpark shell.

How to do it…

Here are the steps to perform this recipe:

  1. Importing Spark data types...

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