DBMS > BigObject vs. Datomic vs. HugeGraph vs. Teradata

System Properties Comparison BigObject vs. Datomic vs. HugeGraph vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

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

A fast-speed and highly-scalable Graph DBMS

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Graph DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

bigobject.io

www.datomic.com

github.com/hugegraph
hugegraph.apache.org

www.teradata.com

Technical documentation

docs.bigobject.io

docs.datomic.com

hugegraph.apache.org/docs

docs.teradata.com

Developer

BigObject, Inc.

Cognitect

Baidu

Teradata

Initial release

2015

2012

2018

1984

Current release

1.0.6735, June 2023

0.9

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

free community edition available

commercial

limited edition free

Open Source

Apache Version 2.0

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

Java

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

Linux
macOS
Unix

hosted
Linux

Data scheme

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

also supports composite index and range index

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL 2016 + extensions

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

Clojure
Java

Groovy
Java
Python

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

Lua

yes

Transaction Functions

asynchronous Gremlin script jobs

yes

UDFs, stored procedures, table functions in parallel

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

depending on used storage backend, e.g. Cassandra and HBase

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

edges in graph

yes

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

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

Users, roles and permissions

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

Teradata is the most popular data warehouse DBMS
2 April 2013, Paul Andlinger

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