  Huge amounts of data being generated continuously by digitally interconnected
systems of humans, organizations and machines. Data comes in variety of formats
including structured, unstructured and semi-structured, what makes it
impossible to apply the same standard approaches, techniques and algorithms to
manage and process this data. Fortunately, the enterprise level distributed
platform named Hadoop Ecosystem exists. This paper explores Apache Hive
component that provides full stack data managements functionality in terms of
Data Definition, Data Manipulation and Data Processing. Hive is a data
warehouse system, which works with structured data stored in tables. Since,
Hive works on top the Hadoop HDSFS, it benefits from extraordinary feature of
HDFS including Fault Tolerance, Reliability, High Availability, Scalability,
etc. In addition, Hive can take advantage of distributed computing power of the
cluster through assigning jobs to MapReduce, Tez and Spark engines to run
complex queries. The paper is focused on studying of Hive Data Model and
analysis of processing performance done by MapReduce and Tez.
