Data quality is crucial for the success of any data-driven organization. In this article, we explore the data quality landscape and how to ensure data quality across all data pipeline stages with Apache Spark. Well discuss the fundamental components of the ETL process and how its the foundation for data analytics and insights. Additionally, we will look at different tools like Databricks Workflows, Azure Data Factory or Apache Airflow, which usually orchestrate the ETL process, and how companies can introduce data quality checks to ensure data is valid, consistent, and trustworthy. Finally, well discuss the importance of a culture that values data quality and encourages every team member to own it to successfully manage data quality in a fast-paced, data-driven environment.
