DBMS > Amazon Neptune vs. Cubrid vs. Google BigQuery vs. Graph Engine vs. Linter

System Properties Comparison Amazon Neptune vs. Cubrid vs. Google BigQuery vs. Graph Engine vs. Linter

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

RDBMS for high security requirements

Primary database model

Graph DBMS
RDF store

Relational DBMS

Graph DBMS
Key-value store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	2.29
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.12
Rank	#350	Overall
	#152	Relational DBMS

Website

aws.amazon.com/neptune

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

cloud.google.com/bigquery

www.graphengine.io

linter.ru

Technical documentation

aws.amazon.com/neptune/developer-resources

cubrid.org/manuals

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

Developer

Amazon

CUBRID Corporation, CUBRID Foundation

Google

Microsoft

relex.ru

Initial release

2017

2008

2010

1990

Current release

11.0, January 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

MIT License

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++, Java

.NET and C

C and C++

Server operating systems

hosted

Linux
Windows

hosted

.NET

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Data scheme

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP/JSON API

RESTful HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

in JavaScript

yes

proprietary syntax with the possibility to convert from PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

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

with encyption-at-rest

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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 and roles can be defined via the AWS Identity and Access Management (IAM)

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)

fine grained access rights according to SQL-standard

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