DBMS > Amazon Redshift vs. BigObject vs. Brytlyt vs. GridDB vs. Rockset

System Properties Comparison Amazon Redshift vs. BigObject vs. Brytlyt vs. GridDB vs. Rockset

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for real-time computations and queries

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

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

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Time Series DBMS

Document store

Secondary database models

Key-value store
Relational DBMS

Relational DBMS
Search engine

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	0.79
Rank	#211	Overall
	#35	Document stores

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

Developer

Amazon (based on PostgreSQL)

BigObject, Inc.

Brytlyt

Toshiba Corporation

Rockset

Initial release

2012

2015

2016

2013

2019

Current release

5.0, August 2023

5.1, August 2022

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

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

commercial

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

C++

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
OS X
Windows

Linux

hosted

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

dynamic typing

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.

ingestion from XML files supported

Secondary indexes

restricted

yes

all fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

SQL92, SQL-like TQL (Toshiba Query Language)

Read-only SQL queries, including JOINs

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

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

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

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

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

user defined functions

in PL/pgSQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

none

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

ACID at container level

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

Access rights for users can be defined per database

Access rights for users and organizations can be defined via Rockset console

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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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.