DBMS > Amazon Redshift vs. BaseX vs. Ignite vs. InfinityDB vs. Interbase

System Properties Comparison Amazon Redshift vs. BaseX vs. Ignite vs. InfinityDB vs. Interbase

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

A Java embedded Key-Value Store which extends the Java Map interface

Light-weight proven RDBMS

originally from Borland

Primary database model

Relational DBMS

Native XML DBMS

Key-value store
Relational DBMS

Key-value store

Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.73
Rank	#142	Overall
	#4	Native XML DBMS

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	4.61
Rank	#72	Overall
	#39	Relational DBMS

Website

aws.amazon.com/redshift

basex.org

ignite.apache.org

boilerbay.com

www.embarcadero.com/products/interbase

Technical documentation

docs.aws.amazon.com/redshift

docs.basex.org

apacheignite.readme.io/docs

boilerbay.com/infinitydb/manual

docs.embarcadero.com/products/interbase

Developer

Amazon (based on PostgreSQL)

BaseX GmbH

Apache Software Foundation

Boiler Bay Inc.

Embarcadero

Initial release

2012

2007

2015

2002

1984

Current release

10.7, August 2023

Apache Ignite 2.6

4.0

InterBase 2020, December 2019

License

Commercial or Open Source

commercial

Open Source

BSD license

Open Source

Apache 2.0

commercial

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

Java

C++, Java, .Net

Java

Server operating systems

hosted

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

All OS with a Java VM

Android
iOS
Linux
OS X
Windows

Data scheme

yes

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

XQuery supports types

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

export as XML data possible

Secondary indexes

restricted

yes

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

JDBC
ODBC

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

ADO.NET
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Actionscript
C
C#
Haskell
Java
JavaScript

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

C#
C++
Java
PHP
Python
Ruby
Scala

Java

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

Server-side scripts

Stored procedures

user defined functions

in Python

yes

yes (compute grid and cache interceptors can be used instead)

yes

Interbase procedure and trigger language

Triggers

yes

via events

yes (cache interceptors and 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

yes (replicated cache)

none

Interbase Change Views

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

ACID

multiple readers, single writer

ACID

Optimistic locking for transactions; no isolation for bulk loads

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept on 4 levels

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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