Recently, in conjunction with the development of a modular, metadata-based ingestion engine that I am developing using Spark, we got into a discussion relating to data validation. Data validation was a natural next step of data ingestion and that is why we came to that topic.
You might be wondering, "What is so special about data validation? Is it because of Spark?" The reason for this article is partly due to Spark, but more importantly due to the fact that it demonstrates the power of Spark and also illustrates the principle that there is more than one method available to achieve our goal.