DBMS > Hive vs. Microsoft Azure Cosmos DB vs. TerminusDB

System Properties Comparison Hive vs. Microsoft Azure Cosmos DB vs. TerminusDB

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Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Globally distributed, horizontally scalable, multi-model database service

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Graph DBMS

Secondary database models

Spatial DBMS

Document store
RDF store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

hive.apache.org

azure.microsoft.com/services/cosmos-db

terminusdb.com

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

learn.microsoft.com/azure/cosmos-db

terminusdb.github.io/terminusdb/#

Developer

Apache Software Foundation

initially developed by Facebook

Microsoft

DataChemist Ltd.

Initial release

2012

2014

2018

Current release

3.1.3, April 2022

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Prolog, Rust

Server operating systems

All OS with a Java VM

hosted

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

SQL-like query language (WOQL)

APIs and other access methods

JDBC
ODBC
Thrift

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

C++
Java
PHP
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

JavaScript
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

JavaScript

yes

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Implicit feature of the cloud service

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory journaling

User concepts

Access control

Access rights for users, groups and roles

Access rights can be defined down to the item level

Role-based access control

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