DBMS > HEAVY.AI vs. OushuDB vs. Snowflake vs. SpatiaLite

System Properties Comparison HEAVY.AI vs. OushuDB vs. Snowflake vs. SpatiaLite

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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

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

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

Spatial extension of SQLite

Primary database model

Relational DBMS

Spatial DBMS

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.10
Rank	#355	Overall
	#154	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	1.63
Rank	#146	Overall
	#3	Spatial DBMS

Website

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

www.oushu.com/product/oushuDB

www.snowflake.com

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

docs.heavy.ai

www.oushu.com/documentation

docs.snowflake.net/manuals/index.html

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

HEAVY.AI, Inc.

Oushu

Snowflake Computing Inc.

Alessandro Furieri

Initial release

2016

2014

2008

Current release

5.10, January 2022

4.0.1, August 2020

5.0.0, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

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

C++ and CUDA

C++

Server operating systems

Linux

hosted

server-less

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

Secondary indexes

yes

SQL

Support of SQL

yes

Full-featured ANSI SQL support

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

JDBC
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C
C++

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

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

fine grained access rights according to SQL-standard

Kerberos, SSL and role based access

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

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