DBMS > GridGain vs. Kinetica vs. MaxDB vs. Microsoft Azure Cosmos DB

System Properties Comparison GridGain vs. Kinetica vs. MaxDB vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

GridGain is an in-memory computing platform, built on Apache Ignite

Fully vectorized database across both GPUs and CPUs

A robust and reliable RDBMS optimized to run all major SAP solutions

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	2.32
Rank	#112	Overall
	#55	Relational DBMS

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

Website

www.gridgain.com

www.kinetica.com

maxdb.sap.com

azure.microsoft.com/services/cosmos-db

Technical documentation

www.gridgain.com/docs/index.html

docs.kinetica.com

maxdb.sap.com/documentation

learn.microsoft.com/azure/cosmos-db

Developer

GridGain Systems, Inc.

Kinetica

SAP, acquired from Software AG (Adabas-D) in 1997

Microsoft

Initial release

2007

2012

1984

2014

Current release

GridGain 8.5.1

7.1, August 2021

7.9.10.12, February 2024

License

Commercial or Open Source

commercial

Limited community edition free

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

C, C++

C++

Server operating systems

Linux
OS X
Solaris
Windows

Linux

AIX
HP-UX
Linux
Solaris
Windows

hosted

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON 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

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

SQL-like DML and DDL statements

yes

SQL-like query language

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
WebDAV

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

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C++
Java
JavaScript (Node.js)
Python

.Net
C#
Java
Perl
PHP
Python

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

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

user defined functions

yes

JavaScript

Triggers

yes (cache interceptors and events)

yes

triggers when inserted values for one or more columns fall within a specified range

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Source-replica replication

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session 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

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

GPU vRAM or System RAM

User concepts

Access control

Security Hooks for custom implementations

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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