DBMS > Altibase vs. Amazon SimpleDB vs. Google BigQuery vs. Oracle Berkeley DB vs. SiteWhere

System Properties Comparison Altibase vs. Amazon SimpleDB vs. Google BigQuery vs. Oracle Berkeley DB vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

Large scale data warehouse service with append-only tables

Widely used in-process key-value store

M2M integration platform for persisting/querying time series data

Primary database model

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Time Series DBMS

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

Score	1.02
Rank	#189	Overall
	#88	Relational DBMS

Score	1.88
Rank	#133	Overall
	#23	Key-value stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Website

altibase.com

aws.amazon.com/simpledb

cloud.google.com/bigquery

www.oracle.com/database/technologies/related/berkeleydb.html

github.com/sitewhere/sitewhere

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

docs.aws.amazon.com/simpledb

cloud.google.com/bigquery/docs

docs.oracle.com/cd/E17076_05/html/index.html

sitewhere1.sitewhere.io/index.html

Developer

Altibase

Amazon

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

SiteWhere

Initial release

1999

2007

2010

1994

2010

Current release

v7.3, 2023, August 2023

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

commercial

Open Source

commercial license available

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

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

C, Java, C++ (depending on the Berkeley DB edition)

Java

Server operating systems

AIX
HP-UX
Linux

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

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

only with the Berkeley DB XML edition

Secondary indexes

yes

All columns are indexed automatically

yes

SQL

Support of SQL

ANSI SQL-92

yes

SQL interfaced based on SQLite is available

APIs and other access methods

JDBC
ODBC
Proprietary protocol

RESTful HTTP API

RESTful HTTP/JSON API

HTTP REST

Supported programming languages

C
C++
Java

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

stored procedures and stored functions

user defined functions

in JavaScript

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding must be implemented in the application

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrent data updates can be detected by the application

Since BigQuery is designed for querying 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

fine grained access rights according to SQL-standard

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

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

Google Cloud Identity & Access Management (IAM)

Users with fine-grained authorization concept

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