Debasish Ghosh explores the role that approximation data structures (Bloom filtera, sketches, HyperLogLog, etc.) play in processing streaming data. Typically, streams are unbounded in space and time, and processing has to be done online using sublinear space. Debasish covers the probabilistic bounds that these data structures offer and shows how they can be used to implement solutions for fast and streaming architectures.
Debasish Ghosh is principal software engineer at Lightbend. Passionate about technology and open source, he loves functional programming and has been trying to learn math and machine learning. Debasish is an occasional speaker in technology conferences worldwide, including the likes of QCon, Philly ETE, Code Mesh, Scala World, Functional Conf, and GOTO. He’s the author of DSLs In Action and Functional & Reactive Domain Modeling. Debasish is a senior member of ACM. He’s also a father, husband, avid reader, and Seinfeld fanboy who loves spending time with his beautiful family.
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