DBMS > Brytlyt vs. GigaSpaces vs. Google BigQuery vs. GridDB

System Properties Comparison Brytlyt vs. GigaSpaces vs. Google BigQuery vs. GridDB

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Editorial information provided by DB-Engines

Name

Description

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

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

Scalable in-memory time series database optimized for IoT and Big Data

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

Key-value store
Relational DBMS

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

Score	0.29
Rank	#288	Overall
	#131	Relational DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Website

brytlyt.io

www.gigaspaces.com

cloud.google.com/bigquery

griddb.net

Technical documentation

docs.brytlyt.io

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

docs.griddb.net

Developer

Brytlyt

Gigaspaces Technologies

Google

Toshiba Corporation

Initial release

2016

2000

2010

2013

Current release

5.0, August 2023

15.5, September 2020

5.1, August 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also 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

C, C++ and CUDA

Java, C++, .Net

C++

Server operating systems

Linux
OS X
Windows

Linux
macOS
Solaris
Windows

hosted

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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.

yes

specific XML-type available, but no XML query functionality.

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-99 for query and DML statements

yes

SQL92, SQL-like TQL (Toshiba Query Language)

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

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

RESTful HTTP/JSON API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

yes

user defined functions

in JavaScript

Triggers

yes

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

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 at container level

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

fine grained access rights 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)

Access rights for users can be defined per database

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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