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Timely and cost-effective analytics over ""big data"" has emerged
as a key ingredient for success in many businesses, scientific and
engineering disciplines, and government endeavors. Web clicks,
social media, scientific experiments, and datacenter monitoring are
among data sources that generate vast amounts of raw data every
day. The need to convert this raw data into useful information has
spawned considerable innovation in systems for large-scale data
analytics, especially over the last decade. Massively Parallel
Databases and MapReduce Systems addresses the design principles and
core features of systems for analyzing very large datasets using
massively-parallel computation and storage techniques on large
clusters of nodes. It first discusses how the requirements of data
analytics have evolved since the early work on parallel database
systems. It then describes some of the major technological
innovations that have each spawned a distinct category of systems
for data analytics. Each unique system category is described along
a number of dimensions including data model and query interface,
storage layer, execution engine, query optimization, scheduling,
resource management, and fault tolerance. It concludes with a
summary of present trends in large-scale data analytics. This is an
ideal reference for anyone with a research or professional interest
in large-scale data analytics.
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