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Data Ingestion with Python Cookbook

You're reading from   Data Ingestion with Python Cookbook A practical guide to ingesting, monitoring, and identifying errors in the data ingestion process

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781837632602
Length 414 pages
Edition 1st Edition
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Author (1):
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Gláucia Esppenchutz Gláucia Esppenchutz
Author Profile Icon Gláucia Esppenchutz
Gláucia Esppenchutz
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Fundamentals of Data Ingestion
2. Chapter 1: Introduction to Data Ingestion FREE CHAPTER 3. Chapter 2: Principals of Data Access – Accessing Your Data 4. Chapter 3: Data Discovery – Understanding Our Data before Ingesting It 5. Chapter 4: Reading CSV and JSON Files and Solving Problems 6. Chapter 5: Ingesting Data from Structured and Unstructured Databases 7. Chapter 6: Using PySpark with Defined and Non-Defined Schemas 8. Chapter 7: Ingesting Analytical Data 9. Part 2: Structuring the Ingestion Pipeline
10. Chapter 8: Designing Monitored Data Workflows 11. Chapter 9: Putting Everything Together with Airflow 12. Chapter 10: Logging and Monitoring Your Data Ingest in Airflow 13. Chapter 11: Automating Your Data Ingestion Pipelines 14. Chapter 12: Using Data Observability for Debugging, Error Handling, and Preventing Downtime 15. Index 16. Other Books You May Enjoy

Importing unstructured data without a schema

As seen before, unstructured data or NoSQL is a group of information that does not follow a format, such as relational or tabular data. It can be presented as an image, video, metadata, transcripts, and so on. The data ingestion process usually involves a JSON file or a document collection, as we previously saw when ingesting data from MongoDB.

In this recipe, we will read a JSON file and transform it into a DataFrame without a schema. Although unstructured data is supposed to have a more flexible design, we will see some implications of not having any schema or structure in our DataFrame.

Getting ready…

Here, we will use the holiday_brazil.json file to create the DataFrame. You can find it in the GitHub repository here: https://github.com/PacktPublishing/Data-Ingestion-with-Python-Cookbook.

We will use SparkSession to read the JSON file and create a DataFrame to ensure the session is up and running.

All code can be...

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