Hadoop 2014 - Big Data (Hadoop) Data & Systems Modeling

The term Big Data highlights high volumes of data. What describes high data volumes for an organization basically dependent on the organization and its (data) history itself. In a nutshell, data management in an organization is focused on delivering data to the appropriate data consumers (people and/or applications) in the most effective and efficient manner as possible. The goal of data quality and data governance is trusted data. The objective of data integration is available data or in other words, delivering the data to the consumers in the proper format. Big Data data & systems modeling can aid in all these aspects. Modeling can be described as the process of creating a simpler, flexible, mathematical representation of a system that may or may not yet exist. Modeling can be used as a powerful communication tool among the technical and business stakeholders and consumers of any (data) project. One of the major aspects of modeling is that the technique is not limited to describing a system solely as the system itself, but various modeling based cases (sensitivity studies) can be conducted to communicate different aspects of possible workload and configuration scenarios.
In any design that involves the movement of data among systems, it is paramount to specify the lineage in the flow of data among the physical data structures, including the mapping and transformation rules necessary to accomplish the project's goals. This level of design requires an understanding of both, the physical implementation as well as the business implication of the data. Data and systems modeling is further used to design data structures at various levels of abstraction from the conceptual to the physical stage. While differentiating between modeling and design, the focus is normally on distinguishing between the logical design and design closer to the physical implementation. Hence, data and systems modeling is basically a necessity for any design. During the Big Data Symposium, actual Hadoop (MapReduce) and Data Analytics models will be presented to further highlight the importance of modeling in any Big Data project.