DBMS > Google BigQuery vs. Hyprcubd vs. openGemini vs. Oracle NoSQL vs. Snowflake

System Properties Comparison Google BigQuery vs. Hyprcubd vs. openGemini vs. Oracle NoSQL vs. Snowflake

Editorial information provided by DB-Engines

Name

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

Description

Large scale data warehouse service with append-only tables

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS

Time Series DBMS

Document store
Key-value store
Relational DBMS

Relational DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.09
Rank	#361	Overall
	#37	Time Series DBMS

Score	3.05
Rank	#97	Overall
	#17	Document stores
	#16	Key-value stores
	#50	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

cloud.google.com/bigquery

hyprcubd.com (offline)

www.opengemini.org
github.com/openGemini

www.oracle.com/database/nosql/technologies/nosql

www.snowflake.com

Technical documentation

cloud.google.com/bigquery/docs

docs.opengemini.org/guide

docs.oracle.com/en/database/other-databases/nosql-database/index.html

docs.snowflake.net/manuals/index.html

Developer

Google

Hyprcubd, Inc.

Huawei and openGemini community

Oracle

Snowflake Computing Inc.

Initial release

2010

2022

2011

2014

Current release

1.1, July 2023

24.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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

Java

Server operating systems

hosted

Linux
Windows

Linux
Solaris SPARC/x86

hosted

Data scheme

yes

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

Integer, Float, Boolean, String

optional

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP/JSON API

gRPC (https)

HTTP REST

RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Go
Java
JavaScript (Node.js)
Python
Rust

C
C#
Go
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

configurable

ACID within a storage node (=shard)

ACID

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

off heap cache

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

token access

Administrators and common users accounts

Access rights for users and roles

Users with fine-grained authorization concept, user roles and pluggable authentication

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