DBMS > Galaxybase vs. HEAVY.AI vs. Microsoft SQL Server vs. SWC-DB vs. Yanza

System Properties Comparison Galaxybase vs. HEAVY.AI vs. Microsoft SQL Server vs. SWC-DB vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Scalable, ACID-compliant native distributed parallel graph platform

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

Microsofts flagship relational DBMS

A high performance, scalable Wide Column DBMS

Time Series DBMS for IoT Applications

Primary database model

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

Time Series DBMS

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

Score	0.00
Rank	#385	Overall
	#40	Graph DBMS

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	0.01
Rank	#378	Overall
	#13	Wide column stores

Website

galaxybase.com

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

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

github.com/kashirin-alex/swc-db
www.swcdb.org

yanza.com

Technical documentation

docs.heavy.ai

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

Developer

Chuanglin(Createlink) Technology Co., Ltd 浙江创邻科技有限公司

HEAVY.AI, Inc.

Microsoft

Alex Kashirin

Yanza

Initial release

2017

2016

1989

2020

2015

Current release

Nov 20, November 2021

5.10, January 2022

SQL Server 2022, November 2022

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

restricted free version is available

Open Source

GPL V3

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

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Implementation language

C and Java

C++ and CUDA

C++

Server operating systems

Linux

Linux
Windows

Linux

Windows

Data scheme

Strong typed schema

yes

schema-free

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

SQL-like query language

APIs and other access methods

Browser interface
console (shell)
Graph API (Gremlin)
OpenCypher
Proprietary native API

JDBC
ODBC
Thrift
Vega

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

Proprietary protocol
Thrift

HTTP API

Supported programming languages

Go
Java
Python

All languages supporting JDBC/ODBC/Thrift
Python

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

C++

any language that supports HTTP calls

Server-side scripts

Stored procedures

user defined procedures and functions

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

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes, but depending on the SQL-Server Edition

none

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

Relationships in graphs

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

Role-based access control

fine grained access rights according to SQL-standard

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