DBMS > Riak KV vs. Sadas Engine vs. SAP SQL Anywhere vs. Snowflake vs. Splice Machine

System Properties Comparison Riak KV vs. Sadas Engine vs. SAP SQL Anywhere vs. Snowflake vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

Distributed, fault tolerant key-value store

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

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

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	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

www.sadasengine.com

www.sap.com/products/technology-platform/sql-anywhere.html

www.snowflake.com

splicemachine.com

Technical documentation

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

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

help.sap.com/docs/SAP_SQL_Anywhere

docs.snowflake.net/manuals/index.html

splicemachine.com/how-it-works

Developer

OpenSource, formerly Basho Technologies

SADAS s.r.l.

SAP

formerly Sybase

Snowflake Computing Inc.

Splice Machine

Initial release

2009

2006

1992

2014

Current release

3.2.0, December 2022

8.0

17, July 2015

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache version 2, commercial enterprise edition

commercial

free trial version available

commercial

Open Source

AGPL 3.0, commercial license 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

Erlang

C++

Java

Server operating systems

Linux
OS X

AIX
Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

hosted

Linux
OS X
Solaris
Windows

Data scheme

schema-free

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

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

APIs and other access methods

HTTP API
Native Erlang Interface

JDBC
ODBC
Proprietary protocol

ADO.NET
HTTP API
JDBC
ODBC

CLI Client
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

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

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

JavaScript (Node.js)
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

Erlang

yes, in C/C++, Java, .Net or Perl

user defined functions

yes

Java

Triggers

yes

pre-commit hooks and post-commit hooks

yes

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

horizontal partitioning

none

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Source-replica replication

Database mirroring

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

yes, multi-version concurrency control (MVCC)

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'

yes

User concepts

Access control

yes, using Riak Security

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

fine grained access rights according to SQL-standard

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

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

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