DBMS > Hive vs. OrigoDB vs. SAP IQ vs. SiteWhere

System Properties Comparison Hive vs. OrigoDB vs. SAP IQ vs. SiteWhere

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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

A fully ACID in-memory object graph database

Columnar RDBMS optimized for Big Data analytics

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Relational DBMS

column-oriented

Time Series DBMS

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

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	2.64
Rank	#108	Overall
	#52	Relational DBMS

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Website

hive.apache.org

origodb.com

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

github.com/sitewhere/sitewhere

Technical documentation

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

origodb.com/docs

help.sap.com/docs/SAP_IQ

sitewhere1.sitewhere.io/index.html

Developer

Apache Software Foundation

initially developed by Facebook

Robert Friberg et al

SAP, formerly Sybase

SiteWhere

Initial release

2012

2009

under the name LiveDB

1994

2010

Current release

3.1.3, April 2022

16.1 SPS04, April 2019

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

commercial

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

All OS with a Java VM

Linux
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

predefined scheme

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC
Thrift

.NET Client API
HTTP API
LINQ

ADO.NET
JDBC
ODBC
RESTful HTTP API

HTTP REST

Supported programming languages

C++
Java
PHP
Python

.Net

C
C#
C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

client side managed; servers are not synchronized

shared disk or shared nothing architectures with SAP IQ Multiplexer

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

SAP/Sybase Replication Server

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

depending on model

yes

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

Write ahead log

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

Role based authorization

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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More resources

DB-Engines blog posts

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