DBMS > Blazegraph vs. FileMaker vs. Google BigQuery vs. Google Cloud Datastore vs. InterSystems IRIS

System Properties Comparison Blazegraph vs. FileMaker vs. Google BigQuery vs. Google Cloud Datastore vs. InterSystems IRIS

Editorial information provided by DB-Engines

Name

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

Description

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

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

Large scale data warehouse service with append-only tables

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

A containerised multi-model DBMS, interoperability and analytics data platform with wide capabilities for vertical and horizontal scalability

Primary database model

Graph DBMS
RDF store

Relational DBMS

Document store

Document store
Key-value store
Object oriented DBMS
Relational DBMS

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

Score	0.81
Rank	#213	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	47.91
Rank	#20	Overall
	#14	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	3.54
Rank	#84	Overall
	#14	Document stores
	#10	Key-value stores
	#1	Object oriented DBMS
	#45	Relational DBMS

Website

blazegraph.com

www.claris.com/filemaker

cloud.google.com/bigquery

cloud.google.com/datastore

www.intersystems.com/products/intersystems-iris

Technical documentation

wiki.blazegraph.com

www.claris.com/resources/documentation

cloud.google.com/bigquery/docs

cloud.google.com/datastore/docs

docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls

Developer

Blazegraph

Claris

a subsidiary of Apple

Google

InterSystems

Initial release

2006

1983

2010

2008

2018

Current release

2.1.5, March 2019

19.4.1, November 2021

2023.3, June 2023

License

Commercial or Open Source

Open Source

extended commercial license 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

Java

Server operating systems

Linux
OS X
Windows

iOS

client part only
Linux
OS X
Windows

hosted

AIX
Linux
macOS
Ubuntu
Windows

Data scheme

schema-free

yes

schema-free

depending on used data model

Typing

predefined data types such as float or date

yes

RDF literal types

yes

yes, details here

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

yes

Secondary indexes

yes

SQL

Support of SQL

SPARQL is used as query language

yes

via plugins

yes

SQL-like query language (GQL)

yes

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

Filemaker WebDirect
JDBC
ODBC

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

using Google App Engine

yes

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication, warm standby

since Version 14

Multi-source replication using Paxos

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

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

User concepts

Access control

Security and Authentication via Web Application Container (Tomcat, Jetty)

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

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

More information provided by the system vendor

Specific characteristics

InterSystems IRIS is a complete cloud-first data platform which includes a multi-model...
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