Databricks Data Quality & QA Engineering Training

Databricks Data Quality & QA Engineering training by Multisoft Virtual Academy is designed to help data professionals ensure accuracy, reliability, and trustworthiness across modern data platforms. As organizations increasingly rely on data-driven decisions, maintaining high data quality and robust testing processes has become critical. This training focuses on building practical skills to validate, monitor, and test data pipelines developed on the Databricks platform. The course covers essential data quality concepts such as data profiling, validation rules, anomaly detection, and quality metrics implementation. Learners gain hands-on experience in applying QA methodologies to batch and streaming data pipelines using Databricks, Apache Spark, and Delta Lake. The program also emphasizes automated testing strategies, including unit testing, integration testing, and regression testing for data workflows, ensuring issues are detected early in the development lifecycle.

Participants will explore real-world scenarios related to data ingestion, transformation, and reporting, learning how to identify data defects, prevent data drift, and maintain consistency across large-scale datasets. In addition, the training addresses governance and monitoring practices, helping teams maintain compliance, improve pipeline stability, and reduce production failures. By the end of the course, learners will be equipped to design and implement scalable data quality and QA frameworks, enabling organizations to confidently deliver reliable, production-ready analytics solutions.