DBMS > Amazon DynamoDB vs. BaseX vs. Cubrid vs. Google Cloud Bigtable vs. Interbase

System Properties Comparison Amazon DynamoDB vs. BaseX vs. Cubrid vs. Google Cloud Bigtable vs. Interbase

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Light-weight proven RDBMS

originally from Borland

Primary database model

Document store
Key-value store

Native XML DBMS

Relational DBMS

Key-value store
Wide column store

Relational DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	1.73
Rank	#142	Overall
	#4	Native XML DBMS

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	4.61
Rank	#72	Overall
	#39	Relational DBMS

Website

aws.amazon.com/dynamodb

basex.org

cubrid.com (korean)
cubrid.org (english)

cloud.google.com/bigtable

www.embarcadero.com/products/interbase

Technical documentation

docs.aws.amazon.com/dynamodb

docs.basex.org

cubrid.org/manuals

cloud.google.com/bigtable/docs

docs.embarcadero.com/products/interbase

Developer

Amazon

BaseX GmbH

CUBRID Corporation, CUBRID Foundation

Google

Embarcadero

Initial release

2012

2007

2008

2015

1984

Current release

10.7, August 2023

11.0, January 2021

InterBase 2020, December 2019

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

BSD license

Open Source

Apache Version 2.0

commercial

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

C, C++, Java

Server operating systems

hosted

Linux
OS X
Windows

Linux
Windows

hosted

Android
iOS
Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

XQuery supports 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.

export as XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

ADO.NET
JDBC
ODBC
OLE DB

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

ADO.NET
JDBC
ODBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

C#
C++
Go
Java
JavaScript (Node.js)
Python

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

Server-side scripts

Stored procedures

yes

Java Stored Procedures

yes

Interbase procedure and trigger language

Triggers

yes

by integration with AWS Lambda

yes

via events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication

Internal replication in Colossus, and regional replication between two clusters in different zones

Interbase Change Views

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

multiple readers, single writer

ACID

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

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

Users with fine-grained authorization concept on 4 levels

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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