DBMS > Google BigQuery vs. HugeGraph vs. JaguarDB vs. Microsoft Azure Table Storage vs. STSdb

System Properties Comparison Google BigQuery vs. HugeGraph vs. JaguarDB vs. Microsoft Azure Table Storage vs. STSdb

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

Performant, highly scalable DBMS for AI and IoT applications

A Wide Column Store for rapid development using massive semi-structured datasets

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS

Graph DBMS

Key-value store
Vector DBMS

Wide column store

Key-value store

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	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

cloud.google.com/bigquery

github.com/hugegraph
hugegraph.apache.org

www.jaguardb.com

azure.microsoft.com/en-us/services/storage/tables

github.com/STSSoft/STSdb4

Technical documentation

cloud.google.com/bigquery/docs

hugegraph.apache.org/docs

www.jaguardb.com/support.html

Developer

Google

Baidu

DataJaguar, Inc.

Microsoft

STS Soft SC

Initial release

2010

2018

2015

2012

2011

Current release

0.9

3.3 July 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GPL V3.0

commercial

Open Source

GPLv2, commercial license available

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

C++

the server part. Clients available in other languages

Server operating systems

hosted

Linux
macOS
Unix

Linux

hosted

Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

also supports composite index and range index

yes

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

RESTful HTTP/JSON API

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC

RESTful HTTP API

.NET Client API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Groovy
Java
Python

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

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

C#
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

asynchronous Gremlin script jobs

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

yes

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

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Multi-source replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

optimistic locking

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

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

rights management via user accounts

Access rights based on private key authentication or shared access signatures

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.