Data are pouring in, and defining and providing data-processing services at massive scale, in short, Big Data services, could significantly improve the revenue of Europe's Small and Medium Enterprises (SMEs). A paradigm shift is about occur, one in which data processing becomes a basic life utility, for both SMEs and the European people. Although the burgeoning datacenter industry, of which the Netherlands is a top player in Europe, is promising to enable Big Data services, the architectures and even infrastructure for these services are still lagging behind in performance, efficiency, and sophistication, and are built as monoliths reminding us of traditional data silos. Can we remove the performance and efficiency limitations of the current Big Data ecosystems, that is, of the complex stacks of middleware that are currently in use, for Big Data services? In this talk, I will present several use cases (workloads) of Big Data services for time-stamped [2,3] and graph data , evaluate or benchmark the performance of several Big Data stacks [3,4] for these use-cases, and present a path (and promising early results) to providing a generic, data-agnostic, non-monolithic Big Data architecture that can efficiently and elastically use datacenter resources via cloud computing interfaces [1,5].
 A. L. Varbanescu and A. Iosup, On Many-Task Big Data Processing: from GPUs to Clouds. Proc. of SC|12 (MTAGS).? http://www.pds.ewi.tudelft.nl/~iosup/many-tasks-big-data-vision13mtags_v100.pdf
 de Ruiter and Iosup. A workload model for MapReduce. MSc thesis at TU Delft. Jun 2012. Available online via TU Delft Library, http://library.tudelft.nl
 Hegeman, Ghit, Capotã, Hidders, Epema, Iosup. The BTWorld Use Case for Big Data Analytics: Description, MapReduce Logical Workflow, and Empirical Evaluation. IEEE Big Data 2013. http://www.pds.ewi.tudelft.nl/~iosup/btworld-mapreduce-workflow13ieeebigdata.pdf
 Y. Guo, M. Biczak, A. L. Varbanescu, A. Iosup, C. Martella, and T. L. Willke. How Well do Graph-Processing Platforms Perform? An Empirical Performance Evaluation and Analysis. IEEE IPDPS 2014. http://www.pds.ewi.tudelft.nl/~iosup/perf-eval-graph-proc14ipdps.pdf
 B. Ghit, N. Yigitbasi, A. Iosup, and D. Epema. Balanced Resource Allocations Across Multiple Dynamic MapReduce Clusters. ACM SIGMETRICS 2014. http://pds.twi.tudelft.nl/~iosup/dynamic-mapreduce14sigmetrics.pdf