DBMS > Bangdb vs. Google BigQuery vs. OrientDB vs. SiriDB vs. SiteWhere

System Properties Comparison Bangdb vs. Google BigQuery vs. OrientDB vs. SiriDB vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

Large scale data warehouse service with append-only tables

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

Open Source Time Series DBMS

M2M integration platform for persisting/querying time series data

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

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

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Website

bangdb.com

cloud.google.com/bigquery

orientdb.org

siridb.com

github.com/sitewhere/sitewhere

Technical documentation

docs.bangdb.com

cloud.google.com/bigquery/docs

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

docs.siridb.com

sitewhere1.sitewhere.io/index.html

Developer

Sachin Sinha, BangDB

Google

OrientDB LTD; CallidusCloud; SAP

Cesbit

SiteWhere

Initial release

2012

2010

2017

2010

Current release

BangDB 2.0, October 2021

3.2.29, March 2024

License

Commercial or Open Source

Open Source

BSD 3

commercial

Open Source

Apache version 2

Open Source

MIT License

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Java

Server operating systems

Linux

hosted

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

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

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

yes

predefined scheme

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

yes

Numeric data

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes

SQL-like query language, no joins

APIs and other access methods

Proprietary protocol
RESTful HTTP API

RESTful HTTP/JSON API

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

HTTP API

HTTP REST

Supported programming languages

C
C#
C++
Java
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java, Javascript

Triggers

yes, Notifications (with Streaming only)

Hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Multi-source replication

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles; record level security configurable

simple rights management via user accounts

Users with fine-grained authorization concept

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