DBMS > Microsoft Azure Table Storage vs. ObjectBox vs. OpenQM vs. Splice Machine

System Properties Comparison Microsoft Azure Table Storage vs. ObjectBox vs. OpenQM vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

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

Extremely fast embedded database for small devices, IoT and Mobile

QpenQM is a high-performance, self-tuning, multi-value DBMS

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Secondary database models

Time Series DBMS

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

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

Score	0.34
Rank	#284	Overall
	#10	Multivalue DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

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

objectbox.io

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

splicemachine.com

Technical documentation

docs.objectbox.io

splicemachine.com/how-it-works

Developer

Microsoft

ObjectBox Limited

Rocket Software, originally Martin Phillips

Splice Machine

Initial release

2012

2017

1993

2014

Current release

3.4-12

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

Open Source

GPLv2, extended commercial license available

Open Source

AGPL 3.0, commercial license available

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

C and C++

Java

Server operating systems

hosted

Android
iOS
Linux
macOS
Windows

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

with some exceptions

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

Proprietary native API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

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

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

.Net
Basic
C
Java
Objective C
PHP
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

online/offline synchronization between client and server

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Access rights based on private key authentication or shared access signatures

yes

Access rights can be defined down to the item level

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

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More resources

Recent citations in the news

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

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

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

How to write data to Azure Table Store with an Azure Function
14 April 2017, Experts Exchange

Inside Azure File Storage
7 October 2015, Microsoft

provided by Google News

Machine learning data pipeline outfit Splice Machine files for insolvency
26 August 2021, The Register

Splice Machine Launches Feature Store to Simplify Feature Engineering
19 January 2021, Datanami

New Splice Machine RDBMS unites OLTP and OLAP
18 November 2015, CIO

Big Data News: Splice Machine, Carpathia, Altiscale, DataGravity
11 February 2014, Data Center Knowledge

Hadoop-based RDBMS Now Available from Splice
12 May 2014, Datanami

provided by Google News

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