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This article introduces SCALPEL3, a scalable open-source framework forstudies involving Large Observational Databases (LODs). Its design easesmedical observational studies thanks to abstractions allowing conceptextraction, high-level cohort manipulation, and production of data formatscompatible with machine learning libraries. SCALPEL3 has successfully been usedon the SNDS database (see Tuppin et al. (2017)), a huge healthcare claimsdatabase that handles the reimbursement of almost all French citizens.  SCALPEL3 focuses on scalability, easy interactive analysis and helpers fordata flow analysis to accelerate studies performed on LODs. It consists ofthree open-source libraries based on Apache Spark. SCALPEL-Flattening allowsdenormalization of the LOD (only SNDS for now) by joining tables sequentiallyin a big table. SCALPEL-Extraction provides fast concept extraction from a bigtable such as the one produced by SCALPEL-Flattening. Finally, SCALPEL-Analysisallows interactive cohort manipulations, monitoring statistics of cohort flowsand building datasets to be used with machine learning libraries. The first twoprovide a Scala API while the last one provides a Python API that can be usedin an interactive environment. Our code is available on GitHub.  SCALPEL3 allowed to extract successfully complex concepts for studies such asMorel et al (2017) or studies with 14.5 million patients observed over threeyears (corresponding to more than 15 billion healthcare events and roughly 15TeraBytes of data) in less than 49 minutes on a small 15 nodes HDFS cluster.SCALPEL3 provides a sharp interactive control of data processing throughlegible code, which helps to build studies with full reproducibility, leadingto improved maintainability and audit of studies performed on LODs.

SCALPEL3: a scalable open-source library for healthcare claims databases