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A scalable, declarative, low-code framework for real-time and batch feature calculation/management (quant finance, anomaly/fraud detection, etc.), predictive ML training/inference and simulation. Built on top of Ray
The aim of this big data project is to design and implement a big data system that can provide real-time context-aware recommendations to drivers on the level of possible danger.
Stream & aggregate tweets containing a set of track terms in memory and write aggregates to rocks db. Aggregates include top hashtags, top mentions and top retweets. Contains a local executable that can run forever, computing aggregates and storing results in a local rocks DB. Also has repl mode for querying results from the db.