DBMS > Datomic vs. Interbase vs. Microsoft Azure Table Storage vs. Microsoft SQL Server vs. OrientDB

System Properties Comparison Datomic vs. Interbase vs. Microsoft Azure Table Storage vs. Microsoft SQL Server vs. OrientDB

Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Light-weight proven RDBMS

originally from Borland

A Wide Column Store for rapid development using massive semi-structured datasets

Microsofts flagship relational DBMS

Multi-model DBMS (Document, Graph, Key/Value)

Primary database model

Relational DBMS

Wide column store

Relational DBMS

Document store
Graph DBMS
Key-value store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	4.61
Rank	#72	Overall
	#39	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Website

www.datomic.com

www.embarcadero.com/products/interbase

azure.microsoft.com/en-us/services/storage/tables

www.microsoft.com/en-us/sql-server

orientdb.org

Technical documentation

docs.datomic.com

docs.embarcadero.com/products/interbase

learn.microsoft.com/en-US/sql/sql-server

www.orientdb.com/docs/last/index.html

Developer

Cognitect

Embarcadero

Microsoft

OrientDB LTD; CallidusCloud; SAP

Initial release

2012

1984

2012

1989

2010

Current release

1.0.6735, June 2023

InterBase 2020, December 2019

SQL Server 2022, November 2022

3.2.29, March 2024

License

Commercial or Open Source

commercial

limited edition free

commercial

restricted free version is available

Open Source

Apache version 2

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java, Clojure

C++

Java

Server operating systems

All OS with a Java VM

Android
iOS
Linux
OS X
Windows

hosted

Linux
Windows

All OS with a Java JDK (>= JDK 6)

Data scheme

yes

schema-free

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

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.

export as XML data possible

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language, no joins

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC

RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Supported programming languages

Clojure
Java

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

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

Interbase procedure and trigger language

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Java, Javascript

Triggers

By using transaction functions

yes

Hooks

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

none

Sharding

Implicit feature of the cloud service

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Interbase Change Views

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes, but depending on the SQL-Server Edition

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

Transaction concepts

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

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

Access rights for users and roles; record level security configurable

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