DBMS > BigObject vs. Ehcache vs. ITTIA vs. Microsoft Azure SQL Database

System Properties Comparison BigObject vs. Ehcache vs. ITTIA vs. Microsoft Azure SQL Database

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

A widely adopted Java cache with tiered storage options

Edge Time Series DBMS with Real-Time Stream Processing

Database as a Service offering with high compatibility to Microsoft SQL Server

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.13
Rank	#329	Overall
	#147	Relational DBMS

Score	4.79
Rank	#66	Overall
	#8	Key-value stores

Score	0.36
Rank	#272	Overall
	#22	Time Series DBMS

Score	72.95
Rank	#16	Overall
	#11	Relational DBMS

Website

bigobject.io

www.ehcache.org

www.ittia.com

azure.microsoft.com/en-us/products/azure-sql/database

Technical documentation

docs.bigobject.io

www.ehcache.org/documentation

docs.microsoft.com/en-us/azure/azure-sql

Developer

BigObject, Inc.

Terracotta Inc, owned by Software AG

ITTIA L.L.C.

Microsoft

Initial release

2015

2009

2007

2010

Current release

3.10.0, March 2022

8.7

V12

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2; commercial licenses available

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 and C++

C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

All OS with a Java VM

Linux
Windows

hosted

Data scheme

yes

schema-free

Fixed schema

yes

Typing

predefined data types such as float or date

yes

Float/Blob/Integer/String/Unicode/Date/Time/Timestamp/Interval

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

SQL-like DML and DDL statements

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JCache

JDBC
ODBC
RESTful HTTP API
WebSocket

ADO.NET
JDBC
ODBC

Supported programming languages

Java

C
C++
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

Lua

yes

Transact SQL

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

by using Terracotta Server

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

by using Terracotta Server

yes

yes, with always 3 replicas available

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

yes

supports JTA and can work as an XA resource

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

yes, multi-version concurrency control (MVCC)

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

Database file passwords

fine grained access rights according to SQL-standard

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More resources

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7 February 2017, Matthias Gelbmann

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