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

Documenting the data discovery process

In recent years, manual data discovery has been rapidly deprecated, giving rise to machine learning and other automated solutions, bringing fast insights into data in storage or online spreadsheets, such as Google Sheets.

Nevertheless, many small companies are just starting out their businesses or data areas, so implementing a paid or cost-related solution might not be a good idea right away. As data professionals, we also need to be malleable when applying the first solution to a problem – there will always be space to improve it later.

Getting ready

This recipe will cover the steps to start the data discovery process effectively. Even though, here, the process is more related to the manual discovery steps, you will see it also applies to the automated ones.

Let’s start by downloading the datasets.

For this recipe, we are going to use the The evolution of genes in viruses and bacteria dataset (https://www.kaggle.com...