DBMS > HEAVY.AI vs. Kyligence Enterprise vs. Microsoft Azure SQL Database vs. Snowflake

System Properties Comparison HEAVY.AI vs. Kyligence Enterprise vs. Microsoft Azure SQL Database vs. Snowflake

Please select another system to include it in the comparison.

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 distributed analytics engine for big data, built on top of Apache Kylin

Database as a Service offering with high compatibility to Microsoft SQL Server

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

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	0.44
Rank	#259	Overall
	#118	Relational DBMS

Score	72.95
Rank	#16	Overall
	#11	Relational DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Website

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

kyligence.io/kyligence-enterprise

azure.microsoft.com/en-us/products/azure-sql/database

www.snowflake.com

Technical documentation

docs.heavy.ai

docs.microsoft.com/en-us/azure/azure-sql

docs.snowflake.net/manuals/index.html

Developer

HEAVY.AI, Inc.

Kyligence, Inc.

Microsoft

Snowflake Computing Inc.

Initial release

2016

2010

2014

Current release

5.10, January 2022

V12

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

Java

C++

Server operating systems

Linux

hosted

Data scheme

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

ANSI SQL for queries (using Apache Calcite)

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Transact SQL

user defined functions

Triggers

yes

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, with always 3 replicas available

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.