  The evolution of the Internet and computer applications have generated
colossal amount of data. They are referred to as Big Data and they consist of
huge volume, high velocity, and variable datasets that need to be managed at
the right speed and within the right time frame to allow real-time data
processing and analysis. Several Big Data solutions were developed, however
they are all based on distributed computing which can be sometimes expensive to
build, manage, troubleshoot, and secure. This paper proposes a novel method for
processing Big Data using memory-based, multi-processing, and one-server
architecture. It is memory-based because data are loaded into memory prior to
start processing. It is multi-processing because it leverages the power of
parallel programming using shared memory and multiple threads running over
several CPUs in a concurrent fashion. It is one-server because it only requires
a single server that operates in a non-distributed computing environment. The
foremost advantages of the proposed method are high performance, low cost, and
ease of management. The experiments conducted showed outstanding results as the
proposed method outperformed other conventional methods that currently exist on
the market. Further research can improve upon the proposed method so that it
supports message passing between its different processes using remote procedure
calls among other techniques.
