  In this work, we detail the design and structure of a Synopses Data Engine
(SDE) which combines the virtues of parallel processing and stream
summarization towards delivering interactive analytics at extreme scale. Our
SDE is built on top of Apache Flink and implements a synopsis-as-a-service
paradigm. In that it achieves (a) concurrently maintaining thousands of
synopses of various types for thousands of streams on demand, (b) reusing
maintained synopses among various concurrent workflows, (c) providing data
summarization facilities even for cross-(Big Data) platform workflows, (d)
pluggability of new synopses on-the-fly, (e) increased potential for workflow
execution optimization. The proposed SDE is useful for interactive analytics at
extreme scales because it enables (i) enhanced horizontal scalability, i.e.,
not only scaling out the computation to a number of processing units available
in a computer cluster, but also harnessing the processing load assigned to each
by operating on carefully-crafted data summaries, (ii) vertical scalability,
i.e., scaling the computation to very high numbers of processed streams and
(iii) federated scalability i.e., scaling the computation beyond single
clusters and clouds by controlling the communication required to answer global
queries posed over a number of potentially geo-dispersed clusters.
