Migrating to Big Data: Contextual Challenges for Data-driven Success

Many database operational and analytical systems are adopting Big Data analytics tools and techniques to meet the upcoming business challenges. Migration to Hadoop Distributed File System has a specialized migration process that maps the current schema models to the batch processing schemes. The database schemas hold many data semantics designed according to the data usage, affordances, access rights, and efficiency demands. Thus, migration towards big data must preserve the base semantics of data according to the data domain, structures, formats, and sources. Most of the time, these dimensions are correlated, especially data structures that are designed according to the usage and formats of data. Here, we have presented a study of major concerns related to the database depending on these dimensions that must be considered while migrating to big data. It provides a literature study for variant case studies of each dimension and their associated qualitative aspects and challenges faced while migrating to big data.