Data transformation — traditionally the domain of IT specialists — is emerging as a critical, widespread problem in data analytics. In the traditional IT toolbox, the specification of data transformations was typically an afterthought, supported by either general-purpose scripting languages, or broad analysis/query languages.
Research has demonstrated that a carefully designed domain-specific language (DSL) for transformation can significantly improve productivity for experts and accessibility for non-experts — particularly if the DSL can be embedded in a workflow with step-by-step visual previews to provide user confidence and satisfaction during transformation.
In this session we discuss the advantages of using a domain-specific language for data transformation tasks. We illustrate these issues with Wrangle, a DSL designed for interactive data transformation.
Joe is Trifacta’s Chief Executive Officer and a Professor of Computer Science at Berkeley. His career in research and industry has focused on data-centric systems and the way they drive computing. In 2010, Fortune Magazine included him in their list of 50 smartest people in technology, and MIT Technology Review magazine included his Bloom language for cloud computing on their TR10 list of the 10 technologies “most likely to change our world”.
Sean is Trifacta’s Chief Technical Officer. He completed his Ph.D. at Stanford University, where his research focused on user interfaces for database systems. At Stanford, Sean led development of new tools for data transformation and discovery, such as Data Wrangler. He previously worked as a data analyst at Citadel Investment Group.