DBMS > Google BigQuery vs. HugeGraph vs. Oracle NoSQL vs. PouchDB vs. Snowflake

System Properties Comparison Google BigQuery vs. HugeGraph vs. Oracle NoSQL vs. PouchDB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A fast-speed and highly-scalable Graph DBMS

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

JavaScript DBMS with an API inspired by CouchDB

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

Primary database model

Relational DBMS

Graph DBMS

Document store
Key-value store
Relational DBMS

Document store

Relational DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

cloud.google.com/bigquery

github.com/hugegraph
hugegraph.apache.org

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

pouchdb.com

www.snowflake.com

Technical documentation

cloud.google.com/bigquery/docs

hugegraph.apache.org/docs

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

pouchdb.com/guides

docs.snowflake.net/manuals/index.html

Developer

Google

Baidu

Oracle

Apache Software Foundation

Snowflake Computing Inc.

Initial release

2010

2018

2011

2012

2014

Current release

0.9

23.3, December 2023

7.1.1, June 2019

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)

Open Source

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

JavaScript

Server operating systems

hosted

Linux
macOS
Unix

Linux
Solaris SPARC/x86

server-less, requires a JavaScript environment (browser, Node.js)

hosted

Data scheme

yes

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

schema-free

yes

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

Typing

predefined data types such as float or date

yes

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

also supports composite index and range index

yes

via views

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP/JSON API

Java API
RESTful HTTP API
TinkerPop Gremlin

RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

CLI Client
JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Groovy
Java
Python

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

JavaScript

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

asynchronous Gremlin script jobs

View functions in JavaScript

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

with a proxy-based framework, named couchdb-lounge

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

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

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

configurable

ACID within a storage node (=shard)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

In-memory capabilities

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

yes

off heap cache

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Users, roles and permissions

Access rights for users and roles

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

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