DBMS > HEAVY.AI vs. HyperSQL vs. Microsoft Azure Synapse Analytics vs. Microsoft SQL Server

System Properties Comparison HEAVY.AI vs. HyperSQL vs. Microsoft Azure Synapse Analytics vs. Microsoft SQL Server

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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

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Microsofts flagship relational DBMS

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	3.14
Rank	#85	Overall
	#46	Relational DBMS

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Website

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

hsqldb.org

azure.microsoft.com/services/synapse-analytics

www.microsoft.com/en-us/sql-server

Technical documentation

docs.heavy.ai

hsqldb.org/web/hsqlDocsFrame.html

docs.microsoft.com/azure/synapse-analytics

learn.microsoft.com/en-US/sql/sql-server

Developer

HEAVY.AI, Inc.

Microsoft

Initial release

2016

2001

2016

1989

Current release

5.10, January 2022

2.7.2, June 2023

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

based on BSD license

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++ and CUDA

Java

C++

Server operating systems

Linux

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

hosted

Linux
Windows

Data scheme

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

APIs and other access methods

JDBC
ODBC
Thrift
Vega

HTTP API

JDBC via HTTP
JDBC
ODBC

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

All languages supporting JDBC/ODBC
Java

C#
Java
PHP

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

Java, SQL

Transact SQL

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Sharding, horizontal partitioning

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

yes

yes, but depending on the SQL-Server Edition

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

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

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

yes

fine grained access rights according to SQL-standard

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