DBMS > DataFS vs. GridGain vs. Riak TS vs. SAP IQ vs. Snowflake

System Properties Comparison DataFS vs. GridGain vs. Riak TS vs. SAP IQ vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

GridGain is an in-memory computing platform, built on Apache Ignite

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Columnar RDBMS optimized for Big Data analytics

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

Primary database model

Object oriented DBMS

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

column-oriented

Relational DBMS

Secondary database models

Graph DBMS

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

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

newdatabase.com

www.gridgain.com

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

www.snowflake.com

Technical documentation

dev.mobiland.com/Overview.xsp

www.gridgain.com/docs/index.html

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

help.sap.com/docs/SAP_IQ

docs.snowflake.net/manuals/index.html

Developer

Mobiland AG

GridGain Systems, Inc.

Open Source, formerly Basho Technologies

SAP, formerly Sybase

Snowflake Computing Inc.

Initial release

2018

2007

2015

1994

2014

Current release

1.1.263, October 2022

GridGain 8.5.1

3.0.0, September 2022

16.1 SPS04, April 2019

License

Commercial or Open Source

commercial

Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

Erlang

Server operating systems

Windows

Linux
OS X
Solaris
Windows

Linux
OS X

AIX
HP-UX
Linux
Solaris
Windows

hosted

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

schema-free

yes

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

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

yes

restricted

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

yes, limited

yes

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

HTTP API
Native Erlang Interface

ADO.NET
JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

.Net
C
C#
C++
VB.Net

C#
C++
Java
PHP
Python
Ruby
Scala

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

C
C#
C++
Java
Perl
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

Erlang

yes

user defined functions

Triggers

no, except callback-events from server when changes happened

yes (cache interceptors and events)

yes

pre-commit hooks and post-commit hooks

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

shared disk or shared nothing architectures with SAP IQ Multiplexer

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

selectable replication factor

SAP/Sybase Replication Server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between datasets 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

User concepts

Access control

Windows-Profile

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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

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