Developing with Model-driven Big Data Analytics-as-a-Service: the Toreador Approach
The Big Data revolution promises to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management. However, major hurdles still need to be overcome on the road that leads to commoditization and wide adoption of Big Data Analytics (BDA). Big Data complexity is the first factor hampering the full potential of BDA. The opacity and variety of Big Data technologies and computations, in fact, make BDA a failure prone and resource-intensive process, which requires a trial-and-error approach. This problem is even exacerbated by the fact that current solutions to Big Data application development take a bottom-up approach, where the last technology release drives application development. Selection of the best Big Data platform, as well as of the best pipeline to execute analytics, represents then a deal breaker. In this tutorial, we propose a return to roots by defining a Model-Driven Engineering (MDE) methodology that supports automation of BDA based on model specification. Our approach lets customers declare requirements to be achieved by an abstract Big Data platform and smart engines deploy the Big Data pipeline carrying out the analytics on a specific instance of such platform. Driven by customers’ requirements, our methodology is based on an OWL-S ontology of Big Data services and on a compiler transforming OWL-S service compositions in workflows that can be directly executed on the selected platform.
The tutorial is intended to highlight the opportunities of our Model-Driven approach by proposing a complete walkthrough on the various steps of our methodology: (i) requirement definition, (ii) bootstrapping and test, and (iii) deployment of a final solution.
The total estimated time for the tutorial is 2 hours. The tutorial will have presentations and demo session. Below is the tutorial outline:
- Introduction to Model-Based Big Data-as-a-Service
- State of the art
- A case study
- Declarative Model Specification
- Procedural Model Specification
- Deployment Model Specification
The tutorial does not require any strong prior knowledge on Big Data technologies.
Claudio Agostino Ardagna, Valerio Bellandi, Paolo Ceravolo, Ernesto Damiani, Jonatan Maggesi