DBMS > Faircom DB vs. FileMaker vs. Google Cloud Spanner vs. Hazelcast

System Properties Comparison Faircom DB vs. FileMaker vs. Google Cloud Spanner vs. Hazelcast

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

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

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

A widely adopted in-memory data grid

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Key-value store

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Website

www.faircom.com/products/faircom-db

www.claris.com/filemaker

cloud.google.com/spanner

hazelcast.com

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

www.claris.com/resources/documentation

cloud.google.com/spanner/docs

hazelcast.org/imdg/docs

Developer

FairCom Corporation

Claris

a subsidiary of Apple

Google

Hazelcast

Initial release

1979

1983

2017

2008

Current release

V12, November 2020

19.4.1, November 2021

5.3.6, November 2023

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Apache Version 2; commercial licenses available

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

ANSI C, C++

Java

Server operating systems

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

iOS

client part only
Linux
OS X
Windows

hosted

All OS with a Java VM

Data scheme

schema free, schema optional, schema required, partial schema,

yes

schema-free

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

yes

via plugins

yes

Query statements complying to ANSI 2011

SQL-like query language

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

Filemaker WebDirect
JDBC
ODBC

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

PHP

Go
Java
JavaScript (Node.js)
Python

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Event Listeners, Executor Services

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Source-replica replication, warm standby

since Version 14

Multi-source replication with 3 replicas for regional instances.

yes

Replicated Map

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

Transaction concepts

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

tunable from ACID to Eventually Consistent

ACID

Strict serializable isolation

one or two-phase-commit; repeatable reads; read commited

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Yes, tunable from durable to delayed durability to in-memory

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

Fine grained access rights according to SQL-standard with additional protections for files

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

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

Role-based access control

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

FairCom kicks off new era of database technology USA - English
10 November 2020, PR Newswire

provided by Google News

Tilda Swinton on 'Problemista' Fan Horror Stories About Her Character
1 March 2024, IndieWire

Reviews - Features
1 March 2024, Reverse Shot

Apple Filed for new Trademarks in the U.S. and Hong Kong this week for the Figurative Version of 'Reality Composer ...
15 October 2023, Patently Apple

Apple Subsidiary to Face Worker's Job Loss Claims, but Not Apple
14 February 2024, Bloomberg Law

Apple subsidiary FileMaker returns to its original name from the '80s
6 August 2019, The Verge

provided by Google News

Google Improves Cloud Spanner: More Compute and Storage without Price Increase
14 October 2023, InfoQ.com

Google turns up the heat on AWS, claims Cloud Spanner is half the cost of DynamoDB
11 October 2023, TechCrunch

Google makes its Cloud Spanner database service faster and more cost-efficient
11 October 2023, SiliconANGLE News

Google Spanner: When Do You Need to Move to It?
11 September 2023, hackernoon.com

Google Cloud Spanner competes directly with Amazon DynamoDB
12 October 2023, Techzine Europe

provided by Google News

Hazelcast Weaves Wider Logic Threads Through The Data Fabric
7 March 2024, Forbes

Hazelcast 5.4 real time data processing platform boosts AI and consistency
17 April 2024, VentureBeat

Hazelcast Achieves Record Year with Leading Brands Choosing Its Platform for Application Modernization, AI Initiatives
22 February 2024, Datanami

Real-Time Data Platform Hazelcast Introduces New Chief Technology Officer Adrian Soars
7 November 2023, Finovate

Hazelcast Versus Redis: A Practical Comparison
4 January 2024, Database Trends and Applications

provided by Google News

Share this page