DBMS > Amazon DocumentDB vs. Google BigQuery vs. IBM Cloudant vs. JanusGraph vs. Tibero

System Properties Comparison Amazon DocumentDB vs. Google BigQuery vs. IBM Cloudant vs. JanusGraph vs. Tibero

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Large scale data warehouse service with append-only tables

Database as a Service offering based on Apache CouchDB

A Graph DBMS optimized for distributed clusters

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

A secure RDBMS, designed for easy portability from Oracle

Primary database model

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Website

aws.amazon.com/documentdb

cloud.google.com/bigquery

www.ibm.com/products/cloudant

janusgraph.org

us.tmaxsoft.com/products/tibero

Technical documentation

aws.amazon.com/documentdb/resources

cloud.google.com/bigquery/docs

cloud.ibm.com/docs/Cloudant

docs.janusgraph.org

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Google

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Linux Foundation; originally developed as Titan by Aurelius

TmaxSoft

Initial release

2019

2010

2017

2003

Current release

0.6.3, February 2023

6, April 2015

License

Commercial or Open Source

commercial

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

Erlang

Java

C and Assembler

Server operating systems

hosted

Linux
OS X
Unix
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

RESTful HTTP/JSON API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
JavaScript
Objective-C
PHP
Ruby

Clojure
Java
Python

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

user defined functions

in JavaScript

View functions (Map-Reduce) in JavaScript

yes

Persistent Stored Procedure (PSM)

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)

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

Multi-source replication
Source-replica replication

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

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

typically not used, however similar functionality with DBRef possible

yes

Relationships in graphs

yes

Transaction concepts

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

Atomic single-document operations

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

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.

planned for next version

User concepts

Access control

Access rights for users and roles

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

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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