DBMS > Ignite vs. JaguarDB vs. Microsoft Azure Table Storage vs. SWC-DB

System Properties Comparison Ignite vs. JaguarDB vs. Microsoft Azure Table Storage vs. SWC-DB

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Editorial information provided by DB-Engines

Name

Description

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Performant, highly scalable DBMS for AI and IoT applications

A Wide Column Store for rapid development using massive semi-structured datasets

A high performance, scalable Wide Column DBMS

Primary database model

Key-value store
Relational DBMS

Key-value store
Vector DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

ignite.apache.org

www.jaguardb.com

azure.microsoft.com/en-us/services/storage/tables

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

apacheignite.readme.io/docs

www.jaguardb.com/support.html

Developer

Apache Software Foundation

DataJaguar, Inc.

Microsoft

Alex Kashirin

Initial release

2015

2012

2020

Current release

Apache Ignite 2.6

3.3 July 2023

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GPL V3.0

commercial

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, Java, .Net

C++

the server part. Clients available in other languages

C++

Server operating systems

Linux
OS X
Solaris
Windows

Linux

hosted

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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

SQL

Support of SQL

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

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

Proprietary protocol
Thrift

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C++

Server-side scripts

Stored procedures

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

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Multi-source replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

optimistic locking

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

rights management via user accounts

Access rights based on private key authentication or shared access signatures

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More resources

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