DBMS > Datomic vs. InterSystems Caché vs. Microsoft Azure Synapse Analytics vs. Microsoft SQL Server vs. NSDb

System Properties Comparison Datomic vs. InterSystems Caché vs. Microsoft Azure Synapse Analytics vs. Microsoft SQL Server vs. NSDb

Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

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

A multi-model DBMS and application server

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Microsofts flagship relational DBMS

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

Primary database model

Relational DBMS

Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Website

www.datomic.com

www.intersystems.com/products/cache

azure.microsoft.com/services/synapse-analytics

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

nsdb.io

Technical documentation

docs.datomic.com

docs.intersystems.com

docs.microsoft.com/azure/synapse-analytics

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

nsdb.io/Architecture

Developer

Cognitect

InterSystems

Microsoft

Initial release

2012

1997

2016

1989

2017

Current release

1.0.7075, December 2023

2018.1.4, May 2020

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

limited edition free

commercial

restricted free version is available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java, Clojure

C++

Java, Scala

Server operating systems

All OS with a Java VM

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

hosted

Linux
Windows

Linux
macOS

Data scheme

yes

depending on used data model

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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 fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

RESTful HTTP API

.NET Client API
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

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

gRPC
HTTP REST
WebSocket

Supported programming languages

Clojure
Java

C#
C++
Java

C#
Java
PHP

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

Java
Scala

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

Transact SQL

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

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

none

Sharding, horizontal partitioning

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

Source-replica replication

yes

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

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

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 for users, groups and roles

yes

fine grained access rights according to SQL-standard

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