  Today's most prominent IT companies are built on the extraction of insight
from data, and data processing has become crucial in data-intensive businesses.
Nevertheless, the size of data which should be processed is growing
significantly fast. The pace of the data growing has changed the nature of data
processing. Today, data-intensive industries demand highly scalable and fault
tolerant data processing architectures which can handle the massive amount of
data. In this paper, we presented a distributed architecture for elastic and
resilient data processing based on the Liquid which is a nearline and offline
big data architecture. We used the Reactive Manifesto to design the
architecture highly reactive to workload changes and failures. We evaluate our
architecture by drawing some numerical comparisons between our architecture
prototype and the Liquid prototype. The performed evaluation shows that our
architecture can be more scalable against workload and more resilient against
failures than the Liquid architecture is.
