DBMS > FileMaker vs. Snowflake vs. Spark SQL vs. VelocityDB

System Properties Comparison FileMaker vs. Snowflake vs. Spark SQL vs. VelocityDB

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Editorial information provided by DB-Engines

Name

Description

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

Cloud-based data warehousing service for structured and semi-structured data

Spark SQL is a component on top of 'Spark Core' for structured data processing

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

Primary database model

Relational DBMS

Graph DBMS
Object oriented DBMS

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

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.05
Rank	#358	Overall
	#36	Graph DBMS
	#16	Object oriented DBMS

Website

www.claris.com/filemaker

www.snowflake.com

spark.apache.org/sql

velocitydb.com

Technical documentation

www.claris.com/resources/documentation

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

velocitydb.com/UserGuide

Developer

Claris

a subsidiary of Apple

Snowflake Computing Inc.

Apache Software Foundation

VelocityDB Inc

Initial release

1983

2014

2011

Current release

19.4.1, November 2021

3.5.0 ( 2.13), September 2023

7.x

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

Scala

Server operating systems

iOS

client part only
Linux
OS X
Windows

hosted

Linux
OS X
Windows

Any that supports .NET

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

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

yes

Secondary indexes

yes

SQL

Support of SQL

yes

via plugins

yes

SQL-like DML and DDL statements

APIs and other access methods

Filemaker WebDirect
JDBC
ODBC

CLI Client
JDBC
ODBC

JDBC
ODBC

.Net

Supported programming languages

PHP

JavaScript (Node.js)
Python

Java
Python
R
Scala

.Net

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

none

yes

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication, warm standby

since Version 14

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

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

Users with fine-grained authorization concept, user roles and pluggable authentication

Based on Windows Authentication

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