Striving for Data Quality: A Practical Approach with Airflow

Data quality is a critical aspect of modern information processing systems. Errors are often discovered too late, once they have already impacted business processes or reporting, leading to significant costs. This article explores the concept of embedding quality control directly into data processing architecture. The author analyzes key metrics necessary for assessing data health and offers practical methods for implementing automated checks using Apache Airflow. Integrating these checks into data pipelines allows for the timely detection of anomalies, preventing incorrect data from reaching analytical models. The article is intended for data engineers and developers looking to improve the reliability of their ETL processes and minimize risks associated with poor-quality information.