DBMS > Google BigQuery vs. Graph Engine vs. IBM Cloudant vs. JaguarDB vs. JanusGraph

System Properties Comparison Google BigQuery vs. Graph Engine vs. IBM Cloudant vs. JaguarDB vs. JanusGraph

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Database as a Service offering based on Apache CouchDB

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

Graph DBMS
Key-value store

Document store

Key-value store
Vector DBMS

Graph DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	2.68
Rank	#106	Overall
	#20	Document stores

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

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Website

cloud.google.com/bigquery

www.graphengine.io

www.ibm.com/products/cloudant

www.jaguardb.com

janusgraph.org

Technical documentation

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

cloud.ibm.com/docs/Cloudant

www.jaguardb.com/support.html

docs.janusgraph.org

Developer

Google

Microsoft

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

DataJaguar, Inc.

Linux Foundation; originally developed as Titan by Aurelius

Initial release

2010

2015

2017

Current release

3.3 July 2023

0.6.3, February 2023

License

Commercial or Open Source

commercial

Open Source

MIT License

commercial

Open Source

GPL V3.0

Open Source

Apache 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

.NET and C

Erlang

C++

the server part. Clients available in other languages

Java

Server operating systems

hosted

.NET

hosted

Linux

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

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

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

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++
F#
Visual Basic

C#
Java
JavaScript
Objective-C
PHP
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

View functions (Map-Reduce) in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

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
Source-replica replication

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

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

Since BigQuery is designed for querying data

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

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

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)

Access rights for users can be defined per database

rights management via user accounts

User authentification and security via Rexster Graph Server

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