This webinar discusses how to make embarrassing data errors a thing of the past.
We will start with how data engineers do not understand their data and have difficulty identifying problematic data records. We will also discuss how the vast majority of data engineers are so busy that they don’t know, or have time to write, tests to write to find data errors. We will finish with a demonstration of DataKitchen’s New DataOps Testgen Product.
That missing piece that connects data system expectations and reality is a ‘Data Journey.’ It is the missing piece of our data systems.
The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure
The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure. Is it overkill? Paradox of choice? Or the right tool for the right job? We discuss.
The Syntax, Semantics, and Pragmatics Gap in Data Quality Validation Testing
What is the full range of data quality validation tests for data at rest and data in use? Linguistics provides an organizing principle: syntax, semantics, and pragmatics