DBMS > Amazon Neptune vs. Google BigQuery vs. ITTIA vs. JanusGraph

System Properties Comparison Amazon Neptune vs. Google BigQuery vs. ITTIA vs. JanusGraph

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Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Large scale data warehouse service with append-only tables

Edge Time Series DBMS with Real-Time Stream Processing

A Graph DBMS optimized for distributed clusters

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

Primary database model

Secondary database models

Relational DBMS

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

Score	2.17
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	50.58
Rank	#19	Overall
	#13	Relational DBMS

Score	0.37
Rank	#269	Overall
	#22	Time Series DBMS

Score	1.78
Rank	#131	Overall
	#12	Graph DBMS

Website

aws.amazon.com/neptune

cloud.google.com/bigquery

www.ittia.com

janusgraph.org

Technical documentation

aws.amazon.com/neptune/developer-resources

cloud.google.com/bigquery/docs

docs.janusgraph.org

Developer

Amazon

Google

ITTIA L.L.C.

Linux Foundation; originally developed as Titan by Aurelius

Initial release

2017

2010

2007

2017

Current release

8.7

1.0.0, October 2023

License

Commercial or Open Source

commercial

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

C and C++

Java

Server operating systems

hosted

Linux
Windows

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

Fixed schema

yes

Typing

predefined data types such as float or date

yes

Float/Blob/Integer/String/Unicode/Date/Time/Timestamp/Interval

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

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API
WebSocket

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
JavaScript (Node.js)
Python

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

yes

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

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

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
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Relationships in graphs

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

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 rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

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

Google Cloud Identity & Access Management (IAM)

Database file passwords

User authentification and security via Rexster Graph Server

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