DBMS > AnzoGraph DB vs. BigObject vs. Datomic vs. DuckDB vs. Teradata Aster

System Properties Comparison AnzoGraph DB vs. BigObject vs. Datomic vs. DuckDB vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

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

Analytic DBMS for real-time computations and queries

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

An embeddable, in-process, column-oriented SQL OLAP RDBMS

Platform for big data analytics on multistructured data sources and types

Primary database model

Graph DBMS
RDF store

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

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

Score	0.22
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.13
Rank	#329	Overall
	#147	Relational DBMS

Score	1.55
Rank	#144	Overall
	#67	Relational DBMS

Score	5.61
Rank	#60	Overall
	#34	Relational DBMS

Website

cambridgesemantics.com/anzograph

bigobject.io

www.datomic.com

duckdb.org

Technical documentation

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

docs.bigobject.io

docs.datomic.com

duckdb.org/docs

Developer

Cambridge Semantics

BigObject, Inc.

Cognitect

Teradata

Initial release

2018

2015

2012

2018

2005

Current release

2.3, January 2021

1.0.7180, July 2024

1.0.0, June 2024

License

Commercial or Open Source

commercial

free trial version available

commercial

free community edition available

commercial

limited edition free

Open Source

MIT License

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, Clojure

C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

All OS with a Java VM

server-less

Linux

Data scheme

Schema-free and OWL/RDFS-schema support

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like DML and DDL statements

yes

APIs and other access methods

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

fluentd
ODBC
RESTful HTTP API

RESTful HTTP API

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C++
Java
Python

Clojure
Java

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

user defined functions and aggregates

Lua

yes

Transaction Functions

R packages

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

But extensive use of caching in the application peers

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

none

But extensive use of caching in the application peers

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

none

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

not needed in graphs

yes

automatically between fact table and dimension tables

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

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More resources

Recent citations in the news

AnzoGraph: A graph database for deep analytics
15 April 2019, InfoWorld

Cambridge Semantics Fits AnzoGraph DB with More Speed, Free Access
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