Wednesday, February 18, 2015

Stream Processing and Probabilistic Methods: Data at Scale

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Stream processing and related abstractions have become all the rage following the rise of systems like Apache Kafka, Samza, and the Lambda architecture. Applying the idea of immutable, append-only event sourcing means we're storing more data than ever before. However, as the cost of storage continues to decline, it's becoming more feasible to store more data for longer periods of time. With immutability, how the data lives isn't interesting anymore. It's all about how it moves.

The shifting landscape of data architecture parallels the way we're designing systems today. Specifically, the evolution of monolithic to service-oriented architecture necessitates a change in the way we do data integration. The traditional normalization approach doesn't cut it. Our systems are composed of databases, caches, search indexes, data warehouses, and a multitude of other components. Moreover, there's an increasing demand for online, real-time processing of this data that's tantamount to the growing popularity of large-scale, offline processing along the lines of Hadoop. This presents an interesting set of new challenges, namely, how do we drink from the firehose without getting drenched?

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