DBMS > Amazon Redshift vs. FileMaker vs. Riak KV vs. Sadas Engine vs. Spark SQL

System Properties Comparison Amazon Redshift vs. FileMaker vs. Riak KV vs. Sadas Engine vs. Spark SQL

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.

Distributed, fault tolerant key-value store

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

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

Primary database model

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational 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	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/redshift

www.claris.com/filemaker

www.sadasengine.com

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/redshift

www.claris.com/resources/documentation

www.tiot.jp/riak-docs/riak/kv/latest

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

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

Developer

Amazon (based on PostgreSQL)

Claris

a subsidiary of Apple

OpenSource, formerly Basho Technologies

SADAS s.r.l.

Apache Software Foundation

Initial release

2012

1983

2009

2006

2014

Current release

19.4.1, November 2021

3.2.0, December 2022

8.0

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache version 2, commercial enterprise edition

commercial

free trial version available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

C++

Scala

Server operating systems

hosted

iOS

client part only
Linux
OS X
Windows

Linux
OS X

AIX
Linux
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

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

restricted

yes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

via plugins

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

Filemaker WebDirect
JDBC
ODBC

HTTP API
Native Erlang Interface

JDBC
ODBC
Proprietary protocol

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

PHP

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

.Net
C
C#
C++
Groovy
Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

no "single point of failure"

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication, warm standby

since Version 14

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

links between data sets can be stored

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

managed by 'Learn by Usage'

User concepts

Access control

fine grained access rights according to SQL-standard

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

yes, using Riak Security

Access rights for users, groups and roles according to SQL-standard

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