At the time of writing, Spark is probably the most popular very large dataset architecture for predictive analytics. Spark is a distributed architecture which helps you manage your large data and makes it easier to analyze. Spark is built upon Hadoop and they share the same filesystem.
However, Spark is not based upon the MapReduce paradigm, and uses the resilient distributed dataset (RDD) structure in order to implement in-memory analytics and manage the parallel processing cluster across all of the nodes of the environment. What that means for analysts is that queries can be very quick, since data is retrieved from memory, which offers much quicker retrieval than disk access. Quicker access means more time for analysis, and less time waiting for results.
Here are some advantages of Spark:
- Spark overcomes some of the limitations of memory-bound analytics, since it...
