DBMS > Amazon Neptune vs. Google BigQuery vs. Hyprcubd vs. Ingres vs. RisingWave

System Properties Comparison Amazon Neptune vs. Google BigQuery vs. Hyprcubd vs. Ingres vs. RisingWave

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

Fast, reliable graph database built for the cloud

Large scale data warehouse service with append-only tables

Serverless Time Series DBMS

Well established RDBMS

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

Primary database model

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	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	0.64
Rank	#238	Overall
	#110	Relational DBMS

Website

aws.amazon.com/neptune

cloud.google.com/bigquery

hyprcubd.com (offline)

www.actian.com/databases/ingres

www.risingwave.com/database

Technical documentation

aws.amazon.com/neptune/developer-resources

cloud.google.com/bigquery/docs

docs.actian.com/ingres

docs.risingwave.com/docs/current/intro

Developer

Amazon

Google

Hyprcubd, Inc.

Actian Corporation

RisingWave Labs

Initial release

2017

2010

1974

originally developed at University Berkely in early 1970s

2022

Current release

11.2, May 2022

1.2, September 2023

License

Commercial or Open Source

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

Rust

Server operating systems

hosted

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Docker
Linux
macOS

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

yes

Standard SQL-types and JSON

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.

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

RESTful HTTP/JSON API

gRPC (https)

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

JDBC
PostgreSQL wire protocol

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

UDFs in Python or Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Ingres Star to access multiple databases simultaneously

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.

Ingres Replicator

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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

MVCC

Durability

Support for making data persistent

yes

with encyption-at-rest

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)

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

token access

fine grained access rights according to SQL-standard

Users and Roles

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