DBMS > GigaSpaces vs. Microsoft Azure Synapse Analytics vs. mSQL

System Properties Comparison GigaSpaces vs. Microsoft Azure Synapse Analytics vs. mSQL

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Editorial information provided by DB-Engines

Name

Description

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

mSQL (Mini SQL) is a simple and lightweight RDBMS

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Secondary database models

Graph DBMS
Search engine

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Trend Chart

Score	0.99
Rank	#187	Overall
	#30	Document stores
	#7	Object oriented DBMS

Score	15.35
Rank	#37	Overall
	#22	Relational DBMS

Score	1.26
Rank	#163	Overall
	#74	Relational DBMS

Website

www.gigaspaces.com

azure.microsoft.com/services/synapse-analytics

hughestech.com.au/products/msql

Technical documentation

docs.gigaspaces.com/latest/landing.html

docs.microsoft.com/azure/synapse-analytics

Developer

Gigaspaces Technologies

Microsoft

Hughes Technologies

Initial release

2000

2016

1994

Current release

15.5, September 2020

4.4, October 2021

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

commercial

free licenses can be provided

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++, .Net

C++

Server operating systems

Linux
macOS
Solaris
Windows

hosted

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

Data scheme

schema-free

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.

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

A subset of ANSI SQL is implemented

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

APIs and other access methods

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

.Net
C++
Java
Python
Scala

C#
Java
PHP

C
C++
Delphi
Java
Perl
PHP
Tcl

Server-side scripts

Stored procedures

yes

Transact SQL

Triggers

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

none

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

ACID

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

Role-based access control

yes

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More resources

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