DBMS > InterSystems Caché vs. Microsoft Azure Cosmos DB vs. Spark SQL vs. Stardog

System Properties Comparison InterSystems Caché vs. Microsoft Azure Cosmos DB vs. Spark SQL vs. Stardog

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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

A multi-model DBMS and application server

Globally distributed, horizontally scalable, multi-model database service

Spark SQL is a component on top of 'Spark Core' for structured data processing

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Key-value store
Object oriented DBMS
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Graph DBMS
RDF store

Secondary database models

Document store

Spatial DBMS

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

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

www.intersystems.com/products/cache

azure.microsoft.com/services/cosmos-db

spark.apache.org/sql

www.stardog.com

Technical documentation

docs.intersystems.com

learn.microsoft.com/azure/cosmos-db

spark.apache.org/docs/latest/sql-programming-guide.html

docs.stardog.com

Developer

InterSystems

Microsoft

Apache Software Foundation

Stardog-Union

Initial release

1997

2014

2010

Current release

2018.1.4, May 2020

3.5.0 ( 2.13), September 2023

7.3.0, May 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

Java

Server operating systems

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

hosted

Linux
OS X
Windows

Linux
macOS
Windows

Data scheme

depending on used data model

schema-free

yes

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

JSON types

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.

yes

Import/export of XML data possible

Secondary indexes

yes

All properties auto-indexed by default

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

SQL-like query language

SQL-like DML and DDL statements

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

.NET Client API
JDBC
ODBC
RESTful HTTP API

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

JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

C#
C++
Java

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

Java
Python
R
Scala

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

JavaScript

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

JavaScript

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Implicit feature of the cloud service

none

Multi-source replication in HA-Cluster

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 in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

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

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users, groups and roles

Access rights can be defined down to the item level

Access rights for users and roles

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