DBMS > AnzoGraph DB vs. FileMaker vs. Microsoft SQL Server vs. Oracle NoSQL

System Properties Comparison AnzoGraph DB vs. FileMaker vs. Microsoft SQL Server vs. Oracle NoSQL

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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

FileMaker is a cross-platform RDBMS that includes a GUI frontend.

Microsofts flagship relational DBMS

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Primary database model

Graph DBMS
RDF store

Relational DBMS

Document store
Key-value store
Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

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

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Website

cambridgesemantics.com/anzograph

www.claris.com/filemaker

www.microsoft.com/en-us/sql-server

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

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

www.claris.com/resources/documentation

learn.microsoft.com/en-US/sql/sql-server

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Cambridge Semantics

Claris

a subsidiary of Apple

Microsoft

Oracle

Initial release

2018

1983

1989

2011

Current release

2.3, January 2021

19.4.1, November 2021

SQL Server 2022, November 2022

23.3, December 2023

License

Commercial or Open Source

commercial

free trial version available

commercial

restricted free version is available

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Java

Server operating systems

Linux

iOS

client part only
Linux
OS X
Windows

Linux
Windows

Linux
Solaris SPARC/x86

Data scheme

Schema-free and OWL/RDFS-schema support

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

Typing

predefined data types such as float or date

yes

optional

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

A http query request can return the data in XML format

yes

Secondary indexes

yes

SQL

Support of SQL

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

yes

via plugins

yes

SQL-like DML and DDL statements

APIs and other access methods

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

Filemaker WebDirect
JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

RESTful HTTP API

Supported programming languages

C++
Java
Python

PHP

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C
C#
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication, warm standby

since Version 14

yes, but depending on the SQL-Server Edition

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

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

off heap cache

User concepts

Access control

Access rights for users and roles

simple rights management via user accounts and connection to external directory services

fine grained access rights according to SQL-standard

Access rights for users and roles

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