DBMS > AnzoGraph DB vs. Firebird vs. Google BigQuery vs. NCache vs. Oracle Rdb

System Properties Comparison AnzoGraph DB vs. Firebird vs. Google BigQuery vs. NCache vs. Oracle Rdb

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

Firebird is an open source RDBMS forked from Borland's InterBase

Large scale data warehouse service with append-only tables

Open-Source and Enterprise in-memory Key-Value Store

Primary database model

Secondary database models

Document store
Search engine

Using distributed Lucene

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	20.50
Rank	#30	Overall
	#18	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.96
Rank	#195	Overall
	#29	Key-value stores

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Website

cambridgesemantics.com/anzograph

www.firebirdsql.org

cloud.google.com/bigquery

www.alachisoft.com/ncache

www.oracle.com/database/technologies/related/rdb.html

Technical documentation

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

www.firebirdsql.org/en/reference-manuals

cloud.google.com/bigquery/docs

www.alachisoft.com/resources/docs

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

Cambridge Semantics

Firebird Foundation

Google

Alachisoft

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2018

2000

As fork of Borland's InterBase

2010

2005

1984

Current release

2.3, January 2021

5.0.0, January 2024

5.3.3, April 2024

7.4.1.1, 2021

License

Commercial or Open Source

commercial

free trial version available

Open Source

Initial Developer's Public License

commercial

Open Source

Enterprise Edition available

commercial

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

C and C++

C#, .NET, .NET Core, Java

Server operating systems

Linux

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

hosted

Linux
Windows

HP Open VMS

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

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

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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

yes

SQL

Support of SQL

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

yes

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

yes

APIs and other access methods

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

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

RESTful HTTP/JSON API

IDistributedCache
JCache
LINQ
Proprietary native API

Supported programming languages

C++
Java
Python

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

PSQL

user defined functions

in JavaScript

support for stored procedures with SQL-Server CLR

Triggers

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication

yes, with selectable consistency level

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

optimistic locking and pessimistic locking

yes, on a single node

Concurrency

Support for concurrent manipulation of data

yes

Features a multi-generational MVCC architecture, readers do not block writers

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

User concepts

Access control

Access rights for users and roles

Users with fine-grained authorization concept

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

Google Cloud Identity & Access Management (IAM)

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

More information provided by the system vendor

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Market metrics

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