The ability to recompute results from raw data at any time is important for data-driven companies to ensure data stability and to selectively incorporate new data into an already delivered data product. However, data transformation processes are heterogeneous and it is possible that manual work of domain experts is part of the process to create a deliverable data product. Domain experts and their work are expensive and time consuming, a recomputation process needs the ability of automatically adding former human interactions. It becomes even more challenging when external systems are used or data changes over time. In this paper, we propose a system architecture which ensures recomputability of results from big data transformation workflows on internal and external systems by using distributed key-value data stores. Furthermore, the system architecture will contain the possibility of incorporating human interactions of former data transformation processes. We will describe how our approach significantly relieves external systems and at the same time increases the performance of the big data transformation workflows.
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This work was partly funded by the German Federal Ministry of Education and Research within the project Competence Center for Scalable Data Services and Solutions (ScaDS) Dresden/Leipzig (BMBF 01IS14014B) and Explicit Privacy-Preserving Host Intrusion Detection System EXPLOIDS (BMBF 16KIS0522K).
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Kricke, M., Grimmer, M. & Schmeißer, M. Preserving Recomputability of Results from Big Data Transformation Workflows. Datenbank Spektrum 17, 245–253 (2017). https://doi.org/10.1007/s13222-017-0265-6
- System architecture