DBMS > Amazon Redshift vs. FileMaker vs. Google Cloud Datastore vs. Percona Server for MongoDB vs. VelocityDB

System Properties Comparison Amazon Redshift vs. FileMaker vs. Google Cloud Datastore vs. Percona Server for MongoDB vs. VelocityDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

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

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Relational DBMS

Document store

Graph DBMS
Object oriented DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	47.91
Rank	#20	Overall
	#14	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.60
Rank	#246	Overall
	#39	Document stores

Score	0.11
Rank	#354	Overall
	#37	Graph DBMS
	#15	Object oriented DBMS

Website

aws.amazon.com/redshift

www.claris.com/filemaker

cloud.google.com/datastore

www.percona.com/mongodb/software/percona-server-for-mongodb

velocitydb.com

Technical documentation

docs.aws.amazon.com/redshift

www.claris.com/resources/documentation

cloud.google.com/datastore/docs

docs.percona.com/percona-distribution-for-mongodb

velocitydb.com/UserGuide

Developer

Amazon (based on PostgreSQL)

Claris

a subsidiary of Apple

Google

Percona

VelocityDB Inc

Initial release

2012

1983

2008

2015

2011

Current release

19.4.1, November 2021

3.4.10-2.10, November 2017

7.x

License

Commercial or Open Source

commercial

Open Source

GPL Version 2

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++

Server operating systems

hosted

iOS

client part only
Linux
OS X
Windows

hosted

Linux

Any that supports .NET

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

via plugins

SQL-like query language (GQL)

APIs and other access methods

JDBC
ODBC

Filemaker WebDirect
JDBC
ODBC

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

proprietary protocol using JSON

.Net

Supported programming languages

All languages supporting JDBC/ODBC

PHP

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

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

.Net

Server-side scripts

Stored procedures

user defined functions

in Python

yes

using Google App Engine

JavaScript

Triggers

yes

Callbacks using the Google Apps Engine

Callbacks are triggered when data changes

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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.

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of 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

via In-Memory Engine

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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)

Access rights for users and roles

Based on Windows Authentication

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