Health Services and health data are critical. Companies are busy analyzing health data and supporting online transaction processing in the same field. Rarely is a Big data solution found that is meant to ensure proper, efficient, and timely processing of health data to improve health-related services. Research is pursued to discover the various requirements of the varied applications of health services on different scales.
Health data is huge, contains different types of data, and is generated and transmitted fast with a requirement to be received and processed fast. There is no assurance that the data may not have any errors. A considerable amount of data is to be processed correctly to respond to different queries. Query response time is very critical. The performance of a system in answering these queries is vital. The data model also plays a role in improving or decreasing the performance of the queries. Depending on the performance, the data model must be modified. It is also important to find out which big data solution is better for handling such queries efficiently.
Hadoop Map-Reduce and Hive technologies have been at the forefront of Big Data development and adoption. Hive provides the original SQL framework on Hadoop. It is a runtime Hadoop support structure that provides Hive Query Language (HQL). Using HQL, various queries can be executed efficiently on large data sets. Here, we are discussing two data platforms, namely Hadoop Map-Reducand Hive, to find which one is reliable and flexible enough to handle large volumes of medical data. The YCSB guidelines should be followed for that.
Nowadays, scientists are willing to work on metrology, genomics, connectomics, and complex physics simulations requiring big data analytics. Big Data analytics is required as the sizes of the data sets are increasing. These datasets are collected from information sources like sensor nodes, remote sensing aerial devices, software logs, cameras, microphones, radars, wireless sensor networks, etc. Processing Exa and terabytes of data is very costly with respect to processing units. The main concept of Big Data processing is to divide the data into small units that can be processed and accumulate the results from all these units after processing. Business intelligence uses descriptive statistics with large amounts of informative data to measure things or detect trends using inductive statistics and concepts from nonlinear system identification. 
Several research papers have been published on performance analysis of various Big data tools. The authors have presented Hive as an open-source data warehousing solution built on top of Hadoop. Hive is a tool that provides a system catalogue which keeps metadata about files within the system. This allows Hive to function as a traditional warehouse which can interface with standard reporting tools like Micro Strategy. A high-performance integrated virtual environment (HIVE) is proposed to have a robust architecture that can perform next-generation sequence data analysis. It is stated to be very strong and flexible due to introducing the abstraction layer between computational requests and operating system processes. It integrates metadata into an object-oriented data model, and thus, it handles all types of data. The honeycomb data model's access control and permission system is secured and hierarchical so that data access privileges can be set easily in a fine granular manner without having multiple rules. Data can be retrieved in HIVE from various sources. Varied visualizations of the result are available with the opportunity to export the results generated to be analyzed further by other external systems. Data can be shared and collaborated across the system because of this data model. Apache Hive is a data warehouse solution built on top of Hadoop for data analysis, summarization, and query on large data sets. It provides the original SQL framework on Hadoop. It gives an abstraction layer on top of Map Reduce, making it easier for analysts and data scientists to query data on the Hadoop File System.HIVE can take data from Hadoop, HDFS (Hadoop File System), and local file systems and write data to all of these. HBase allows random reads and writes. HIVE offers efficient data aggregation, analysis, and ad hoc querying of massive data sets. HIVE converts HQL queries to Directed Acyclic Graphs of Map-Reduce jobs. These Map Reduce jobs are executed in Hadoop.
Custom Map Reduce scripts can be plugged into queries. Data can be queried in custom formats using existing IO libraries. It has a Metastore containing schemas and statistics to explore data effectively. It supports primitive data types within tables and supports array, map and nested structures. It works with text, binary and column-oriented file formats.