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Architecture of Hive

• User interface
  

Hive offers interaction between the users and the Hadoop distributed file system. Some of the user interfaces supported by Hive are the Hive command line, Hive Web UI, and Windows server’s Hive HD insight.

•  Meta Store
  

Hive stores metadata of tables, databases, etc., or schema in respective database stores. These stores are known as Meta stores.

• HiveQL Process Engine
  

This component works the same as the SQL for querying schema information on the Meta store. It is one of the developments that are meant to replace the traditional approach employed by a MapReduce program. HiveQL process engine allows users to write a query for a MapReduce task and process it rather than coding a MapReduce program in Java.

• Execution Engine
  

This is the conjunction segment of the HiveQL process engine and Hive’s MapReduce. It is tasked with processing a query and generating results similar to MapReduce results.

• HBASE or HDFS
  

HBASE or HDFS is a method for storing data in the file system.

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Hiring our apache hive homework helpers is the best decision you can ever make when you are stuck with a project based on Hadoop. The Hadoop ecosystem contains Hive and other sub-projects. It is an open-source framework that stores and analyzes big data in a distributed platform. Hadoop has two main modules:

Hadoop Distributed File System (HDFS)

This module supports the storage and processing of datasets. It boasts of a fault-tolerant file system that runs on commodity hardware.

MapReduce

This module is a parallel programming model. It processes large amounts of data on huge clusters of commodity hardware. The data processed by MapReduce can be structured, unstructured, and semi-structured. There are two main concepts that you should be familiar with in MapReduce:
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•    Map function
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2.    MapReduce API
•    Mapper class
•    Reducer class
•    Job class
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Remarkable help with apache hive homework based on column types

Column types are used in the hive to create tables. The following are some of the column types that support column data types in the hive:

Integral types

Integral type, INT is used to specify data of type integer. BIGINT and SMALLINT are used when the integer range is bigger and smaller respectively. Also, TINYINT is used when the range is the least.

String types

They are usually specified using single or double-quotes. String types have two data types, CHAR and VARCHAR. C-types escape characters are supported in Hive.
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