DBMS > Hive vs. Infobright vs. Microsoft SQL Server vs. OrientDB vs. SiteWhere

System Properties Comparison Hive vs. Infobright vs. Microsoft SQL Server vs. OrientDB vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

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

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Microsofts flagship relational DBMS

Multi-model DBMS (Document, Graph, Key/Value)

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store

Time Series DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	1.02
Rank	#192	Overall
	#90	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	3.25
Rank	#89	Overall
	#16	Document stores
	#6	Graph DBMS
	#13	Key-value stores

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Website

hive.apache.org

ignitetech.com/softwarelibrary/infobrightdb

www.microsoft.com/en-us/sql-server

orientdb.org

github.com/sitewhere/sitewhere

Technical documentation

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

learn.microsoft.com/en-US/sql/sql-server

www.orientdb.com/docs/last/index.html

sitewhere1.sitewhere.io/index.html

Developer

Apache Software Foundation

initially developed by Facebook

Ignite Technologies Inc.; formerly InfoBright Inc.

Microsoft

OrientDB LTD; CallidusCloud; SAP

SiteWhere

Initial release

2012

2005

1989

2010

Current release

3.1.3, April 2022

SQL Server 2022, November 2022

3.2.29, March 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

commercial

restricted free version is available

Open Source

Apache version 2

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Java

Server operating systems

All OS with a Java VM

Linux
Windows

All OS with a Java JDK (>= JDK 6)

Linux
OS X
Windows

Data scheme

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

predefined scheme

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like query language, no joins

APIs and other access methods

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

HTTP REST

Supported programming languages

C++
Java
PHP
Python

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Java, Javascript

Triggers

yes

Hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

yes, but depending on the SQL-Server Edition

Multi-source replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

fine grained access rights according to SQL-standard

Access rights for users and roles; record level security configurable

Users with fine-grained authorization concept

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