DBMS > DataFS vs. Ehcache vs. Infobright vs. Microsoft Azure Table Storage

System Properties Comparison DataFS vs. Ehcache vs. Infobright vs. Microsoft Azure Table Storage

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

A widely adopted Java cache with tiered storage options

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

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

Primary database model

Secondary database models

Graph DBMS

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

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	0.96
Rank	#194	Overall
	#91	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Website

newdatabase.com

www.ehcache.org

ignitetech.com/softwarelibrary/infobrightdb

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

Technical documentation

dev.mobiland.com/Overview.xsp

www.ehcache.org/documentation

Developer

Mobiland AG

Terracotta Inc, owned by Software AG

Ignite Technologies Inc.; formerly InfoBright Inc.

Microsoft

Initial release

2018

2009

2005

2012

Current release

1.1.263, October 2022

3.10.0, March 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

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

Server operating systems

Windows

All OS with a Java VM

Linux
Windows

hosted

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

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.

Secondary indexes

Knowledge Grid Technology used instead

SQL

Support of SQL

yes

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

JCache

ADO.NET
JDBC
ODBC

RESTful HTTP API

Supported programming languages

.Net
C
C#
C++
VB.Net

Java

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Server-side scripts

Stored procedures

Triggers

no, except callback-events from server when changes happened

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

by using Terracotta Server

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

Source-replica 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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

supports JTA and can work as an XA resource

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

Windows-Profile

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

Access rights based on private key authentication or shared access signatures

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.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Atlassian asks customers to patch critical Jira vulnerability
22 July 2021, BleepingComputer

Critical Jira Flaw in Atlassian Could Lead to RCE
22 July 2021, Threatpost

DZone Coding Java JBoss 5 to 7 in 11 steps
9 January 2014, DZone

provided by Google News

Working with Azure to Use and Manage Data Lakes
7 March 2024, Simplilearn

Azure Cosmos DB Data Migration tool imports from Azure Table storage | Azure updates
5 May 2015, Microsoft

How to Use C# Azure.Data.Tables SDK with Azure Cosmos DB
9 July 2021, hackernoon.com

Quick Guide to Azure Storage Pricing
16 May 2023, DevOps.com

How to use Azure Table storage in .Net
14 January 2019, InfoWorld

provided by Google News

Share this page