DBMS > AnzoGraph DB vs. FileMaker vs. IBM Db2 Event Store

System Properties Comparison AnzoGraph DB vs. FileMaker vs. IBM Db2 Event Store

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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.

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Graph DBMS
RDF store

Relational DBMS

Event Store
Time Series 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	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Website

cambridgesemantics.com/anzograph

www.claris.com/filemaker

www.ibm.com/products/db2-event-store

Technical documentation

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

www.claris.com/resources/documentation

www.ibm.com/docs/en/db2-event-store

Developer

Cambridge Semantics

Claris

a subsidiary of Apple

IBM

Initial release

2018

1983

2017

Current release

2.3, January 2021

19.4.1, November 2021

2.0

License

Commercial or Open Source

commercial

free trial version available

commercial

free developer edition available

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

C and C++

Server operating systems

Linux

iOS

client part only
Linux
OS X
Windows

Linux

Linux, macOS, Windows for the developer addition

Data scheme

Schema-free and OWL/RDFS-schema support

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

A http query request can return the data in XML format

Secondary indexes

yes

SQL

Support of SQL

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

yes

via plugins

yes

through the embedded Spark runtime

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
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

C++
Java
Python

PHP

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

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

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

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Eventual Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

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

fine grained access rights according to SQL-standard

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