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

Product type Paperback

Published in May 2023

Publisher Packt

ISBN-13 9781837632602

Length 414 pages

Edition 1st Edition

Languages

Python

Tools

Apache Airflow

Concepts

Data Engineering

Author (1):

Gláucia Esppenchutz

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Table of Contents (17) Chapters

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

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16. Other Books You May Enjoy

Ingesting Data from Structured and Unstructured Databases

Nowadays, we can store and retrieve data from multiple sources, and the optimal storage method depends on the type of information being processed. For example, most APIs make data available in an unstructured format as this allows the sharing of data of multiple formats (for example, audio, video, and image) and has low storage costs via the use of data lakes. However, if we want to make quantitative data available for use with several tools to support analysis, then the most reliable option might be structured data.

Ultimately, whether you are a data analyst, scientist, or engineer, it is essential to understand how to manage both structured and unstructured data.

In this chapter, we will cover the following recipes: