PAW: A Platform for Analytics Workflows

Big Data analytics in science and industry are performed on a range of heterogeneous data stores, both traditional and modern, and on a diversity of query engines. Workflows are difficult to design and implement since they span a variety of systems. To reduce development time and processing costs, automation is needed. We present PAW, a platform to manage analytics workflows. PAW enables workflow design, execution, analysis and optimization with respect to time efficiency, over multiple execution engines, namely a DBMS, a MapReduce engine, and an orchestration engine. This configuration is emerging as a common paradigm used to combine analysis of unstructured data with analysis of structured data (e.g., NoSQL plus SQL). The demonstration of PAW focuses on the usability of the platform by users with various expertise, the automation of the analysis and optimization of execution, as well as the effect of optimization on workflow execution. The demonstration scenarios are based on synthetic and real workflows on real data.