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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

Using log-level types

Now that we have been introduced to how and where to insert logs, let’s understand log types or levels. Each log level has its own degree of relevance inside any system. For instance, the console output does not show debug messages by default.

We already covered how to log levels using PySpark in the Inserting formatted SparkSession logs to facilitate your work recipe in Chapter 6. Now we will do the same using only Python. This recipe aims to show how to set logging levels at the beginning of your script and insert the different levels inside your code to create a hierarchy of priority for your logs. With this, you can create a structured script that allows you or your team to monitor and identify errors.

Getting ready

We will use only Python code. Make sure you have Python version 3.7 or above. You can use the following command on your CLI to check your version:

$ python3 –-version
Python 3.8.10

The following code execution can be...

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