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

Using SQL operators for data quality

Good data quality is crucial for an organization to ensure the effectiveness of its data systems. By performing quality checks within the DAG, it is possible to stop pipelines and notify stakeholders before erroneous data is introduced into a production lake or warehouse.

Although plenty of available tools in the market provide data quality checks, one of the most popular ways to do this is by running SQL queries. As you may have already guessed, Airflow has providers to support those operations.

This recipe will cover the data quality principal topics in the data ingestion process, pointing out the best SQLOperator type to run in those situations.

Getting ready

Before starting our exercise, let’s create a simple Entity Relationship Diagram (ERD) for a customers table. You can see here how it looks:

Figure 10.40 – An example of customers table columns

Figure 10.40 – An example of customers table columns

And the same table is represented with...