DBMS > AnzoGraph DB vs. BigchainDB vs. Google BigQuery vs. TinkerGraph vs. Warp 10

System Properties Comparison AnzoGraph DB vs. BigchainDB vs. Google BigQuery vs. TinkerGraph vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Large scale data warehouse service with append-only tables

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

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

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.13
Rank	#345	Overall
	#35	Graph DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

cambridgesemantics.com/anzograph

www.bigchaindb.com

cloud.google.com/bigquery

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

www.warp10.io

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

bigchaindb.readthedocs.io/en/latest

cloud.google.com/bigquery/docs

www.warp10.io/content/02_Getting_started

Developer

Cambridge Semantics

Google

SenX

Initial release

2018

2016

2010

2009

2015

Current release

2.3, January 2021

License

Commercial or Open Source

commercial

free trial version available

Open Source

AGPL v3

commercial

Open Source

Apache 2.0

Open Source

Apache License 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

Python

Java

Server operating systems

Linux

hosted

Linux
OS X
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

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

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

yes

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

CLI Client
RESTful HTTP API

RESTful HTTP/JSON API

TinkerPop 3

HTTP API
Jupyter
WebSocket

Supported programming languages

C++
Java
Python

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Groovy
Java

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in JavaScript

yes

WarpScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

selectable replication factor

none

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

none

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

not needed 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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

yes

optional

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 rights for users and roles

yes

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

Google Cloud Identity & Access Management (IAM)

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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