DBMS > HEAVY.AI vs. Oracle Rdb vs. Snowflake vs. Ultipa

System Properties Comparison HEAVY.AI vs. Oracle Rdb vs. Snowflake vs. Ultipa

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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

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

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Relational DBMS

Graph DBMS

Secondary database models

Spatial DBMS

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

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.19
Rank	#330	Overall
	#30	Graph DBMS

Website

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

www.oracle.com/database/technologies/related/rdb.html

www.snowflake.com

www.ultipa.com

Technical documentation

docs.heavy.ai

www.oracle.com/database/technologies/related/rdb-doc.html

docs.snowflake.net/manuals/index.html

www.ultipa.com/document

Developer

HEAVY.AI, Inc.

Oracle, originally developed by Digital Equipment Corporation (DEC)

Snowflake Computing Inc.

Ultipa

Initial release

2016

1984

2014

2019

Current release

5.10, January 2022

7.4.1.1, 2021

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

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

Server operating systems

Linux

HP Open VMS

hosted

Data scheme

yes

Flexible Schema (defined schema, partial schema, 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

SQL

Support of SQL

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

CLI Client
JDBC
ODBC

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript (Node.js)
Python

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes, on a single node

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

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

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