DBMS > BigObject vs. DataFS vs. IBM Db2 Event Store vs. mSQL

System Properties Comparison BigObject vs. DataFS vs. IBM Db2 Event Store vs. mSQL

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

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.

Distributed Event Store optimized for Internet of Things use cases

mSQL (Mini SQL) is a simple and lightweight RDBMS

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Object oriented DBMS

Event Store
Time Series DBMS

Relational DBMS

Secondary database models

Graph DBMS

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Website

bigobject.io

newdatabase.com

www.ibm.com/products/db2-event-store

hughestech.com.au/products/msql

Technical documentation

docs.bigobject.io

dev.mobiland.com/Overview.xsp

www.ibm.com/docs/en/db2-event-store

Developer

BigObject, Inc.

Mobiland AG

IBM

Hughes Technologies

Initial release

2015

2018

2017

1994

Current release

1.1.263, October 2022

2.0

4.4, October 2021

License

Commercial or Open Source

commercial

free community edition available

commercial

free developer edition available

commercial

free licenses can be provided

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C and 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)

Windows

Linux

Linux, macOS, Windows for the developer addition

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

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

yes

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

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

through the embedded Spark runtime

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

.NET Client API
Proprietary client DLL
WinRT client

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

Supported programming languages

.Net
C
C#
C++
VB.Net

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

C
C++
Delphi
Java
Perl
PHP
Tcl

Server-side scripts

Stored procedures

Lua

yes

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

none

Proprietary Sharding system

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Active-active shard replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

none

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

No - written data is immutable

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

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

Advancements in streaming data storage, real-time analysis and machine learning
25 July 2019, ibm.com

How IBM Is Turning Db2 into an ‘AI Database’
3 June 2019, Datanami

Best cloud databases of 2022
4 October 2022, ITPro

Why a robust data management strategy is essential today | IBM HDM
19 September 2019, Express Computer

provided by Google News

Higher Education PS rules out ghost students before PAC
29 November 2018, diggers.news

provided by Google News

Share this page