DBMS > AnzoGraph DB vs. Apache Druid vs. BigObject vs. Oracle

System Properties Comparison AnzoGraph DB vs. Apache Druid vs. BigObject vs. Oracle

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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

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Analytic DBMS for real-time computations and queries

Widely used RDBMS

Primary database model

Graph DBMS
RDF store

Relational DBMS
Time Series DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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	2.85
Rank	#96	Overall
	#50	Relational DBMS
	#6	Time Series DBMS

Score	0.13
Rank	#329	Overall
	#147	Relational DBMS

Score	1286.59
Rank	#1	Overall
	#1	Relational DBMS

Website

cambridgesemantics.com/anzograph

druid.apache.org

bigobject.io

www.oracle.com/database

Technical documentation

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

druid.apache.org/docs/latest/design

docs.bigobject.io

docs.oracle.com/en/database

Developer

Cambridge Semantics

Apache Software Foundation and contributors

BigObject, Inc.

Oracle

Initial release

2018

2012

2015

1980

Current release

2.3, January 2021

30.0.0, June 2024

23c, September 2023

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache license v2

commercial

free community edition available

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

Server operating systems

Linux

Linux
OS X
Unix

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-less columns are supported

yes

Schemaless in JSON and XML columns

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

SQL

Support of SQL

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

SQL for querying

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

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

JDBC
RESTful HTTP/JSON API

fluentd
ODBC
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C++
Java
Python

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Server-side scripts

Stored procedures

user defined functions and aggregates

Lua

PL/SQL

also stored procedures in Java possible

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

manual/auto, time-based

none

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes, via HDFS, S3 or other storage engines

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

Access rights for users and roles

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

fine grained access rights according to SQL-standard

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