DBMS > GigaSpaces vs. Google BigQuery vs. Microsoft Azure SQL Database vs. Stardog vs. TypeDB

System Properties Comparison GigaSpaces vs. Google BigQuery vs. Microsoft Azure SQL Database vs. Stardog vs. TypeDB

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

Large scale data warehouse service with append-only tables

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

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

TypeDB is a strongly-typed database with a rich and logical type system and TypeQL as its query language

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Graph DBMS
RDF store

Graph DBMS
Relational DBMS

Often described as a 'hyper-relational' database, since it implements the 'Entity-Relationship Paradigm' to manage complex data structures and ontologies.

Secondary database models

Graph DBMS
Search engine

Document store
Graph DBMS
Spatial DBMS

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

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	0.65
Rank	#234	Overall
	#20	Graph DBMS
	#107	Relational DBMS

Website

www.gigaspaces.com

cloud.google.com/bigquery

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

www.stardog.com

typedb.com

Technical documentation

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

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

docs.stardog.com

typedb.com/docs

Developer

Gigaspaces Technologies

Google

Microsoft

Stardog-Union

Vaticle

Initial release

2000

2010

2016

Current release

15.5, September 2020

V12

7.3.0, May 2020

2.26.3, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

GPL Version 3, commercial licenses 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

Java, C++, .Net

C++

Java

Server operating systems

Linux
macOS
Solaris
Windows

hosted

Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free and OWL/RDFS-schema support

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

yes

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

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

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

gRPC protocol
TypeDB Console (shell)
TypeDB Studio (Visualisation software- previously TypeDB Workbase)

Supported programming languages

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

All JVM based languages
Groovy
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Transact SQL

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes, event driven architecture

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

by using Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes, with always 3 replicas available

Multi-source replication in HA-Cluster

Multi-source replication

by using Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

by using Apache Kafka and Apache Zookeeper

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Immediate Consistency in HA-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

substituted by the relationship feature

Transaction concepts

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

ACID

Since BigQuery is designed for querying 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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Access rights for users and roles

yes

at REST API level; other APIs in progress

More information provided by the system vendor

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Licensing and pricing models

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