The latest Ophidia release is v1.0.0 (released in March 2017). The current release includes already some of the extensions developed during the project, e.g. the higher decoupling between the analytical framework and the I/O features. The next version is expected by July-August 2017. Additional releases will follow during 2017.
It has been extended and used in several research projects like: FP7 EUBRazilCloudConnect, FP7 CLIP-C and H2020 INDIGO-DataCloud
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Specific needs / Value Proposition:
It addresses big data challenges in eScience. It exploits advanced parallel computing techniques and a hierarchical storage organization to execute intensive data analysis over multi-terabytes datasets.
Specific benefits / Value Proposition:
The service provides a mix of features that makes it very interesting in the big data landscape.
Basically it provides support for high performance data analytics in eScience contexts, which means that:
- It supports metadata management (including provenance) which is key in eScience contexts;
- It can be deployed both in HPC and private cloud environments;
- It implements a high performance data analytics approach;
- It provides declarative interfaces for analytics workflow;
- It supports in-memory data analytics;
- It provides a user-friendly Python API (client-side);
- It implements a novel approach in scientific contexts like climate change, still relying on sequential tools for data analysis.