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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

Applying schemas to analytical data

In the previous chapter, we saw how to apply schemas to structured and unstructured data, but the application of a schema is not limited to raw files.

Even when working with already processed data, there will be cases when we need to cast the values of a column or change column names to be used by another department. In this recipe, we will learn how to apply a schema to Parquet files and how it works.

Getting ready

We will need SparkSession for this recipe. Ensure you have a session that is up and running. We will use the same dataset as in the Ingesting Parquet files recipe.

Feel free to execute the code using a Jupyter notebook or your PySpark shell session.

How to do it…

Here are the steps to perform this recipe:

  1. Looking at our columns: As seen in the Ingesting Parquet files recipe, we can list the columns and their inferred data types. You can see the list as follows:
     VendorID: long
     tpep_pickup_datetime...

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