DBMS > HEAVY.AI vs. Linter vs. Riak KV vs. Sadas Engine

System Properties Comparison HEAVY.AI vs. Linter vs. Riak KV vs. Sadas Engine

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Editorial information provided by DB-Engines

Name

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

RDBMS for high security requirements

Distributed, fault tolerant key-value store

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

Primary database model

Relational DBMS

Key-value store

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

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	1.48
Rank	#146	Overall
	#70	Relational DBMS

Score	0.12
Rank	#309	Overall
	#142	Relational DBMS

Score	6.29
Rank	#69	Overall
	#10	Key-value stores

Score	0.00
Rank	#357	Overall
	#155	Relational DBMS

Website

github.com/heavyai/heavydb
www.heavy.ai

linter.ru/en

riak.com/products/riak-kv

www.sadasengine.com

Technical documentation

docs.heavy.ai

www.sadasengine.com/index.php/downloads/documentation-columnar-db

Developer

HEAVY.AI, Inc.

relex.ru/en

Basho Technologies

SADAS s.r.l.

Initial release

2016

1990

2009

2006

Current release

5.10, January 2022

2.1.0, April 2015

8.0

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

Apache version 2, commercial enterprise edition

commercial

free trial version available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++ and CUDA

C and C++

Erlang

C++

Server operating systems

Linux

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux
OS X

AIX
Linux
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.

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

HTTP API
Native Erlang Interface

JDBC
ODBC
Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

Server-side scripts

Stored procedures

yes

proprietary syntax with the possibility to convert from PL/SQL

JavaScript and Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Sharding

no "single point of failure"

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

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

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

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

More information provided by the system vendor

Specific characteristics

Optimizes memory and compute layers to deliver unprecedented performance. Natively...
» more

Competitive advantages

Only HEAVY.AI allows users to visually and interactively discover high-value patterns,...
» more

Typical application scenarios

Advanced analytics, geospatial and time-series analytics, accelerated data science,...
» more

Licensing and pricing models

HEAVY.AI Free (Free Forever), Foundations ($2k/month), and Enterprise Licensure (Inquire...
» more

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More resources

Recent citations in the news

The insideBIGDATA IMPACT 50 List for Q3 2022
19 July 2022, insideBIGDATA

OmniSci Gets HEAVY New Name and New CEO
1 March 2022, Datanami

HEAVY.AI Introduces New Capabilities that Enable Organizations to Gain Faster Access to Rapidly Growing Data Sets and Deliver Decision-Quality Information
23 March 2022, insideBIGDATA

The insideBIGDATA IMPACT 50 List for Q2 2022
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The insideBIGDATA IMPACT 50 List for Q1 2022
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23 December 2011, InfoQ.com

Basho Revamps Riak Open-Source Database
17 September 2014, InformationWeek

Basho, Maker of Riak NoSQL Database, Raises $25M
13 January 2015, Data Center Knowledge

provided by Google News

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