DBMS > Google BigQuery vs. JaguarDB vs. JanusGraph vs. Oracle Rdb

System Properties Comparison Google BigQuery vs. JaguarDB vs. JanusGraph vs. Oracle Rdb

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Performant, highly scalable DBMS for AI and IoT applications

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Primary database model

Relational DBMS

Key-value store
Vector DBMS

Graph 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	0.06
Rank	#381	Overall
	#59	Key-value stores
	#15	Vector DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Website

cloud.google.com/bigquery

www.jaguardb.com

janusgraph.org

www.oracle.com/database/technologies/related/rdb.html

Technical documentation

cloud.google.com/bigquery/docs

www.jaguardb.com/support.html

docs.janusgraph.org

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

Google

DataJaguar, Inc.

Linux Foundation; originally developed as Titan by Aurelius

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2010

2015

2017

1984

Current release

3.3 July 2023

0.6.3, February 2023

7.4.1.1, 2021

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

Open Source

Apache 2.0

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++

the server part. Clients available in other languages

Java

Server operating systems

hosted

Linux

Linux
OS X
Unix
Windows

HP Open VMS

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Clojure
Java
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate 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

Since BigQuery is designed for querying data

ACID

yes, on a single node

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes

In-memory capabilities

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

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

rights management via user accounts

User authentification and security via Rexster Graph Server

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