DBMS > Amazon Redshift vs. IRONdb vs. Microsoft Access vs. Realm vs. Splice Machine

System Properties Comparison Amazon Redshift vs. IRONdb vs. Microsoft Access vs. Realm vs. Splice Machine

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Large scale data warehouse service for use with business intelligence tools

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

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

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	105.40
Rank	#11	Overall
	#8	Relational DBMS

Score	7.71
Rank	#52	Overall
	#9	Document stores

Score	0.54
Rank	#255	Overall
	#116	Relational DBMS

Website

aws.amazon.com/redshift

www.circonus.com/solutions/time-series-database/

www.microsoft.com/en-us/microsoft-365/access

realm.io

splicemachine.com

Technical documentation

docs.aws.amazon.com/redshift

docs.circonus.com/irondb/category/getting-started

developer.microsoft.com/en-us/access

realm.io/docs

splicemachine.com/how-it-works

Developer

Amazon (based on PostgreSQL)

Circonus LLC.

Microsoft

Realm, acquired by MongoDB in May 2019

Splice Machine

Initial release

2012

2017

1992

2014

Current release

V0.10.20, January 2018

1902 (16.0.11328.20222), March 2019

3.1, March 2021

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

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

C and C++

C++

Java

Server operating systems

hosted

Linux

Windows

Not a real database server, but making use of DLLs

Android
Backend: server-less
iOS
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

but not compliant to any SQL standard

yes

APIs and other access methods

JDBC
ODBC

HTTP API

ADO.NET
DAO
ODBC
OLE DB

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

.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

yes, in Lua

yes

since Access 2010 using the ACE-engine

runs within the applications so server-side scripts are unnecessary

yes

Java

Triggers

yes

since Access 2010 using the ACE-engine

yes

Change Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

configurable replication factor, datacenter aware

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

Immediate consistency per node, eventual consistency across nodes

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

but no files for transaction logging

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

but no files for transaction logging

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

a simple user-level security was built in till version Access 2003

yes

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

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