DBMS > Brytlyt vs. Cubrid vs. Google BigQuery vs. IBM Cloudant vs. NSDb

System Properties Comparison Brytlyt vs. Cubrid vs. Google BigQuery vs. IBM Cloudant vs. NSDb

Editorial information provided by DB-Engines

Name

Description

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

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Large scale data warehouse service with append-only tables

Database as a Service offering based on Apache CouchDB

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Primary database model

Relational DBMS

Document store

Time Series DBMS

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

Score	0.27
Rank	#292	Overall
	#132	Relational DBMS

Score	0.97
Rank	#189	Overall
	#86	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	2.85
Rank	#95	Overall
	#18	Document stores

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Website

brytlyt.io

cubrid.com (korean)
cubrid.org (english)

cloud.google.com/bigquery

www.ibm.com/products/cloudant

nsdb.io

Technical documentation

docs.brytlyt.io

cubrid.org/manuals

cloud.google.com/bigquery/docs

cloud.ibm.com/docs/Cloudant

nsdb.io/Architecture

Developer

Brytlyt

CUBRID Corporation, CUBRID Foundation

Google

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Initial release

2016

2008

2010

2017

Current release

5.0, August 2023

11.0, January 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 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

C, C++ and CUDA

C, C++, Java

Erlang

Java, Scala

Server operating systems

Linux
OS X
Windows

Linux
Windows

hosted

Linux
macOS

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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.

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

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

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP/JSON API

gRPC
HTTP REST
WebSocket

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
JavaScript
Objective-C
PHP
Ruby

Java
Scala

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

Java Stored Procedures

user defined functions

in JavaScript

View functions (Map-Reduce) in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

atomic operations within a document possible

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

fine grained access rights according to SQL-standard

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

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