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Data scientists use a wide variety of systems with a wide variety of user interfaces such as spreadsheets and notebooks for their data exploration, discovery, preprocessing, and analysis tasks. While this wide selection of tools offers data scientists the freedom to pick the right tool for each task, each of these tools has limitations (e.g., the lack of reproducibility of notebooks), data needs to be translated between tool-specific formats, and common functionality such as versioning, provenance, and dealing with data errors often has to be implemented for each system. We argue that rather than alternating between task-specific tools, a superior approach is to build multiple user-interfaces on top of a single incremental workflow / dataflow platform with built-in support for versioning, provenance, error & tracking, and data cleaning. We discuss Vizier, a notebook system that implements this approach, introduce the challenges that arose in building such a system, and highlight how our work on Vizier lead to novel research in uncertain data management and incremental execution of workflows. 

Data we encounter in the real-world such as printed menus, business documents, and nutrition labels, are often ad-hoc. Valuable insights can be gathered from this data when combined with additional information. Recent advances in computer vision and augmented reality have made it possible to understand and enrich such data. Joining real-world data with remote data stores and surfacing those enhanced results in place, within an augmented reality interface can lead to better and more informed decision-making capabilities. However, building end-user applications that perform these joins with minimal human effort is not straightforward. It requires a diverse set of expertise, including machine learning, database systems, computer vision, and data visualization. To address this complexity, we present Quill – a framework to develop end-to-end applications that model augmented reality applications as a join between real- world data and remote data stores. Using an intuitive domain-specific language, Quill accelerates the development of end-user applications that join real-world data with remote data stores. Through experiments on applications from multiple different domains, we show that Quill not only expedites the process of development, but also allows developers to build applications that are more performant than those built using standard developer tools, thanks to the ability to optimize declarative specifications. We also perform a user-focused study to investigate how easy (or difficult) it is to use Quill for developing augmented reality applications than other existing tools. Our results show that Quill allows developers to build and deploy applications with a lower technical background than building the same application using existing developer tools.