Google BigQuery provides access to a massive cloud-based relational database with the potential to significantly enhance the way the scientific community uses information. BigQuery can process billions of rows and terabytes of data in tens of seconds, and scale to petabytes of data at very low costs. Data providers can contribute database tables for public access or protect their tables with Access Control Lists (ACLs) for private or subscriber only access. Data from tables uploaded by different providers can be jointly analyzed using SQL, and kept updated by each provider so users can focus on analyzing data instead of maintaining database infrastructure. To date much of the early content in BigQuery was of a non-scientific nature; accelerated efforts to increase the scientific content are underway. While BigQuery has broad applications across multiple domains, this talk will focus on efforts to develop and populate BigQuery with datasets relevant to the life sciences - chemistry and pharmaceuticals in particular. Resources under development will be presented to demonstrate how this publicly available platform and content can be leveraged to address questions that are difficult to answer otherwise. Use cases will demonstrate cross-mapping scientific content from disparate sources with capabilities beyond what is easily done today.

Google BigQuery for analysis of scientific datasets

2019 Fall National Meeting

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