DBMS > EXASOL vs. GigaSpaces vs. Google BigQuery vs. Microsoft Azure SQL Database vs. Warp 10

System Properties Comparison EXASOL vs. GigaSpaces vs. Google BigQuery vs. Microsoft Azure SQL Database vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Time Series DBMS

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	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	76.78
Rank	#16	Overall
	#11	Relational DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

www.exasol.com

www.gigaspaces.com

cloud.google.com/bigquery

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

www.warp10.io

Technical documentation

www.exasol.com/resources

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

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

www.warp10.io/content/02_Getting_started

Developer

Exasol

Gigaspaces Technologies

Google

Microsoft

SenX

Initial release

2000

2010

2015

Current release

15.5, September 2020

V12

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

Apache License 2.0

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
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

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

yes

SQL-99 for query and DML statements

yes

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

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

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

HTTP API
Jupyter
WebSocket

Supported programming languages

Java
Lua
Python
R

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions

in JavaScript

Transact SQL

yes

WarpScript

Triggers

yes

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on HBase

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

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

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

based on HBase

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

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

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

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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