DBMS > Datomic vs. Microsoft Azure Cosmos DB vs. Microsoft SQL Server vs. NSDb vs. SQream DB

System Properties Comparison Datomic vs. Microsoft Azure Cosmos DB vs. Microsoft SQL Server vs. NSDb vs. SQream DB

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

Globally distributed, horizontally scalable, multi-model database service

Microsofts flagship relational DBMS

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

www.datomic.com

azure.microsoft.com/services/cosmos-db

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

nsdb.io

sqream.com

Technical documentation

docs.datomic.com

learn.microsoft.com/azure/cosmos-db

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

nsdb.io/Architecture

docs.sqream.com

Developer

Cognitect

Microsoft

SQream Technologies

Initial release

2012

2014

1989

2017

Current release

1.0.6735, June 2023

SQL Server 2022, November 2022

2022.1.6, December 2022

License

Commercial or Open Source

commercial

limited edition free

commercial

restricted free version is available

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java, Clojure

C++

Java, Scala

C++, CUDA, Haskell, Java, Scala

Server operating systems

All OS with a Java VM

hosted

Linux
Windows

Linux
macOS

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

yes

yes: int, bigint, decimal, string

yes, ANSI Standard SQL Types

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

Secondary indexes

yes

All properties auto-indexed by default

yes

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language

yes

APIs and other access methods

RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

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

gRPC
HTTP REST
WebSocket

.Net
JDBC
ODBC

Supported programming languages

Clojure
Java

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

Java
Scala

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

JavaScript

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

user defined functions in Python

Triggers

By using transaction functions

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

Implicit feature of the cloud service

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

Sharding

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

Implicit feature of the cloud service

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

yes

Using Apache Lucene

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

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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