DBMS > Amazon Redshift vs. Fujitsu Enterprise Postgres vs. Realm vs. Splice Machine

System Properties Comparison Amazon Redshift vs. Fujitsu Enterprise Postgres vs. Realm vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

Document store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.31
Rank	#285	Overall
	#129	Relational DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

aws.amazon.com/redshift

www.postgresql.fastware.com

realm.io

splicemachine.com

Technical documentation

docs.aws.amazon.com/redshift

www.postgresql.fastware.com/product-manuals

realm.io/docs

splicemachine.com/how-it-works

Developer

Amazon (based on PostgreSQL)

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

Realm, acquired by MongoDB in May 2019

Splice Machine

Initial release

2012

2014

Current release

Fujitsu Enterprise Postgres 14, January 2022

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

AGPL 3.0, commercial license available

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

Server operating systems

hosted

Linux
Windows

Android
Backend: server-less
iOS
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
Java

with Android only
Objective-C
React Native
Swift

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

runs within the applications so server-side scripts are unnecessary

yes

Java

Triggers

yes

Change Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

partitioning by range, list and by hash

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

In-Memory realm

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

Specific characteristics

100% compatible with community PostgreSQL
» more

Competitive advantages

Built-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
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Typical application scenarios

Transactional payments applications, reporting and mixed workloads.
» more

Market metrics

Over 30 years experience in database technology. Over 20 years in Postgres development...
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Licensing and pricing models

Core based licensing
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