  Modern data science applications increasingly use heterogeneous data sources
and analytics. This has led to growing interest in polystore systems,
especially analytical polystores. In this work, we focus on emerging multi-data
model analytics workloads over social media data that fluidly straddle
relational, graph, and text analytics. Instead of a generic polystore, we build
a "tri-store" system that is more aware of the underlying data models to better
optimize execution to improve scalability and runtime efficiency. We name our
system AWESOME (Analytics WorkbEnch for SOcial MEdia). It features a powerful
domain-specific language named ADIL. ADIL builds on top of underlying query
engines (e.g., SQL and Cypher) and features native data types for succinctly
specifying cross-engine queries and NLP operations, as well as automatic
in-memory and query optimizations. Using real-world tri-model analytical
workloads and datasets, we empirically demonstrate the functionalities of
AWESOME for scalable data science over social media data and evaluate its
efficiency.
