DBMS > GigaSpaces vs. Google BigQuery vs. Microsoft SQL Server vs. Oracle

System Properties Comparison GigaSpaces vs. Google BigQuery vs. Microsoft SQL Server vs. Oracle

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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

Large scale data warehouse service with append-only tables

Microsofts flagship relational DBMS

Widely used RDBMS

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Secondary database models

Graph DBMS
Search engine

Document store
Graph DBMS
Spatial DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Website

www.gigaspaces.com

cloud.google.com/bigquery

www.microsoft.com/en-us/sql-server

www.oracle.com/database

Technical documentation

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

learn.microsoft.com/en-US/sql/sql-server

docs.oracle.com/en/database

Developer

Gigaspaces Technologies

Google

Microsoft

Oracle

Initial release

2000

2010

1989

1980

Current release

15.5, September 2020

SQL Server 2022, November 2022

23c, September 2023

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

commercial

restricted free version is available

commercial

restricted free version is 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++

C and C++

Server operating systems

Linux
macOS
Solaris
Windows

hosted

Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Data scheme

schema-free

yes

Schemaless in JSON and XML columns

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

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

with proprietary extensions

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
OLE DB
Tabular Data Stream (TDS)

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

PL/SQL

also stored procedures in Java possible

Triggers

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

isolation level can be parameterized

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

Version 12c introduced the new option 'Oracle Database In-Memory'

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

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