DBMS > GridGain vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. Netezza

System Properties Comparison GridGain vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. Netezza

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

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

Search-as-a-service for web and mobile app development

Globally distributed, horizontally scalable, multi-model database service

Data warehouse and analytics appliance part of IBM PureSystems

Primary database model

Key-value store
Relational DBMS

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Vector DBMS

Spatial DBMS

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

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	5.52
Rank	#59	Overall
	#6	Search engines

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

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Website

www.gridgain.com

azure.microsoft.com/en-us/services/search

azure.microsoft.com/services/cosmos-db

www.ibm.com/products/netezza

Technical documentation

www.gridgain.com/docs/index.html

learn.microsoft.com/en-us/azure/search

learn.microsoft.com/azure/cosmos-db

Developer

GridGain Systems, Inc.

Microsoft

IBM

Initial release

2007

2015

2014

2000

Current release

GridGain 8.5.1

License

Commercial or Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, C++, .Net

Server operating systems

Linux
OS X
Solaris
Windows

hosted

Linux

included in appliance

Data scheme

yes

schema-free

yes

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

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

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

SQL-like query language

yes

APIs and other access methods

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

RESTful HTTP API

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

JDBC
ODBC
OLE DB

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C#
Java
JavaScript
Python

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

C
C++
Fortran
Java
Lua
Perl
Python
R

Server-side scripts

Stored procedures

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

JavaScript

yes

Triggers

yes (cache interceptors and events)

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

Implicit feature of the cloud service

yes

Implicit feature of the cloud service

Source-replica replication

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*

yes

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

Foreign keys

Referential integrity

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

Security Hooks for custom implementations

yes

using Azure authentication

Access rights can be defined down to the item level

Users with fine-grained authorization concept

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.