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

Reading a CSV file

A CSV file is a plain text file where commas separate each data point, and each line represents a new record. It is widely used in many areas, such as finance, marketing, and sales, to store data. Software such as Microsoft Excel and LibreOffice, and even online solutions such as Google Spreadsheets, provide reading and writing operations for this file. Visually it resembles a structured table, which greatly enhances the file’s usability.

Getting ready

You can download the CSV dataset for this from Kaggle. Use this link to download the file: https://www.kaggle.com/datasets/jfreyberg/spotify-chart-data. We are going to use the same Spotify dataset as in Chapter 2.

Note

Since Kaggle is a dynamic platform, the filename might change occasionally. After downloading it, I named the file spotify_data.csv.

For this recipe, we will use only Python and Jupyter Notebook to execute the code and create a more friendly visualization.

How to do it...