Data has never been as significant as it is today. It can be acquired virtually at will on any subject. Yet, this poses new challenges towards data management, especially in terms of storage (data is not consumed during processing, i. e., the data volume keeps growing), flexibility (new applications emerge), and operability (analysts are no IT experts). The goal has to be a demand-driven data provisioning, i. e., the right data must be available in the right form at the right time. Therefore, we introduce a tailorable data preparation zone for Data Lakes called BARENTS. It enables users to model in an ontology how to derive information from data and assign the information to use cases. The data is automatically processed based on this model and the refined data is made available to the appropriate use cases. Here, we focus on a resource-efficient data management strategy. BARENTS can be embedded seamlessly into established Big Data infrastructures, e. g., Data Lakes.

\n The Internet of Things is applied in many domains and collects vast\n amounts of data. This data provides access to a lot of knowledge\n when analyzed comprehensively. However, advanced analysis techniques\n such as predictive or prescriptive analytics require access to both,\n history data, i.e., long-term persisted data, and real-time data as\n well as a joint view on both types of data. State-of-the-art hybrid\n processing architectures for big data - namely, the Lambda and the\n Kappa Architecture - support the processing of history data and\n real-time data. However, they lack of a tight coupling of the two\n processing modes. That is, the user has to do a lot of work manually\n in order to enable a comprehensive analysis of the data. For\n instance, the user has to combine the results of both processing\n modes or apply knowledge from one processing mode to the other.\n Therefore, we introduce a novel hybrid processing architecture for\n big data, called BRAID. BRAID intertwines the processing of history\n data and real-time data by adding communication channels between the\n batch engine and the stream engine. This enables to carry out\n comprehensive analyses automatically at a reasonable overhead.\n