  In the current era of Big Data, data engineering has transformed into an
essential field of study across many branches of science. Advancements in
Artificial Intelligence (AI) have broadened the scope of data engineering and
opened up new applications in both enterprise and research communities.
Aggregations (also termed reduce in functional programming) are an integral
functionality in these applications. They are traditionally aimed at generating
meaningful information on large data-sets, and today, they are being used for
engineering more effective features for complex AI models. Aggregations are
usually carried out on top of data abstractions such as tables/ arrays and are
combined with other operations such as grouping of values. There are frameworks
that excel in the said domains individually. But, we believe that there is an
essential requirement for a data analytics tool that can universally integrate
with existing frameworks, and thereby increase the productivity and efficiency
of the entire data analytics pipeline. Cylon endeavors to fulfill this void. In
this paper, we present Cylon's fast and scalable aggregation operations
implemented on top of a distributed in-memory table structure that universally
integrates with existing frameworks.
