DBMS > Amazon Redshift vs. AnzoGraph DB vs. BigchainDB vs. TinkerGraph vs. Vertica

System Properties Comparison Amazon Redshift vs. AnzoGraph DB vs. BigchainDB vs. TinkerGraph vs. Vertica

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

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

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	0.08
Rank	#348	Overall
	#35	Graph DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

aws.amazon.com/redshift

cambridgesemantics.com/anzograph

www.bigchaindb.com

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

www.vertica.com

Technical documentation

docs.aws.amazon.com/redshift

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

bigchaindb.readthedocs.io/en/latest

vertica.com/documentation

Developer

Amazon (based on PostgreSQL)

Cambridge Semantics

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2012

2018

2016

2009

2005

Current release

2.3, January 2021

12.0.3, January 2023

License

Commercial or Open Source

commercial

free trial version available

Open Source

AGPL v3

Open Source

Apache 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Python

Java

C++

Server operating systems

hosted

Linux

Data scheme

yes

Schema-free and OWL/RDFS-schema support

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

restricted

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

does not fully support an SQL-standard

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

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

JDBC
ODBC

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

CLI Client
RESTful HTTP API

TinkerPop 3

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
Python

Go
Haskell
Java
JavaScript
Python
Ruby

Groovy
Java

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication in MPP-Cluster

selectable replication factor

none

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in MPP-Cluster

none

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

not needed in graphs

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

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

fine grained access rights according to SQL-standard

Access rights for users and roles

yes

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Licensing and pricing models

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