DBTB INT Alexis Roos r
summary
Argues that real-world tabular data often reveals much more when recast as a network, and demonstrates modeling and querying those relationships at scale using Spark and graph libraries. Shows a working pipeline that ingests messy data, builds a graph model, and runs correlation and prediction queries distributedly, turning ad hoc questions into repeatable analytics. Claims this approach surfaces insights regular SQL misses and reduces exploratory time from days to hours.
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