Managing Massive Health Records using Partitioning in HIVE

Modernizing healthcare industry's move towards processing massive health records, and to access those for analysis and put into action will greatly increases the complexities. [1] Due to the growing unstructured nature of Big Data form health industry, it is necessary to structure and emphasis its size into nominal value with possible solution. Healthcare industry faces many challenges that make us to know the importance to develop the data analytics. In this paper we presented that how HIVE (hierarchy of international vengeance and extermination) processed and analyzed the diabetic data set with the help of SQL like HIVEQL. HIVE reduced the complexity to many folds. No need to write big and complex programs in java. HIVE structured the data and also queried the data in a very small amount of the time. HIVE managed big data easily and supported data loading, tables, partitions, join aggregation etc. HIVE includes a system catalog- Metastore that contains schemas and statistics which proved beneficial in processing, analyzing big data, query optimization and query compilation. Analyses of the diabetic data to perform the Outpatient Monitoring and Management of Insulin Dependent Diabetes Mellitus (IDDM) set using HIVE as a warehousing tool resulted in providing an efficient way to cure and care the patients and in deriving some interesting facts such as helping the hospital management to arrange the medical equipments, staff, labs etc based on the frequency of the arrival of the patients on daily, monthly as well as yearly basis.