DBMS > GigaSpaces vs. Google BigQuery vs. Snowflake vs. Spark SQL vs. Transbase

System Properties Comparison GigaSpaces vs. Google BigQuery vs. Snowflake vs. Spark SQL vs. Transbase

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

Cloud-based data warehousing service for structured and semi-structured data

Spark SQL is a component on top of 'Spark Core' for structured data processing

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Secondary database models

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

www.gigaspaces.com

cloud.google.com/bigquery

www.snowflake.com

spark.apache.org/sql

www.transaction.de/en/products/transbase.html

Technical documentation

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

www.transaction.de/en/products/transbase/features.html

Developer

Gigaspaces Technologies

Google

Snowflake Computing Inc.

Apache Software Foundation

Transaction Software GmbH

Initial release

2000

2010

2014

1987

Current release

15.5, September 2020

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

Apache 2.0

commercial

free development license

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

Scala

C and C++

Server operating systems

Linux
macOS
Solaris
Windows

hosted

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

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

RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript (Node.js)
Python

Java
Python
R
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

user defined functions

yes

Triggers

yes, event driven architecture

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

none

Source-replica replication

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

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

yes

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)

Users with fine-grained authorization concept, user roles and pluggable authentication

fine grained access rights according to SQL-standard

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