Lambda Architecture - Real Time Machine Learning

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Query = Function (All Data)

Nathan Marz, the creator of Storm at Twitter quoted the above years before. In a nutshell, this is the essence of Lambda Architecture.

Lambda architectures present distinctive traits that make them particularly suitable for real-world, large-scale data products. They provide a viable answer to the need of gathering insights that are function of the entire data set. At the same time, lambda architectures are built with the capability to support data processes at different data and temporal granularity. Enterprise data pipelines, most of the time, comprise heterogeneous processing modalities: computing can be batch (e.g. Hadoop) vs. (near) real-time vs. streaming (e.g. Storm), data storage can he file-oriented (e.g. HDFS) vs. columnar (e.g. HBase). Additionally, given the distributed nature of lambda architectures, they are designed to be scalable and fault-tolerant.

Once again, hear from my experience, another feather to your big data crown.

See you all, and as usual register early.