DBMS > DataFS vs. HEAVY.AI vs. Microsoft Azure SQL Database vs. NSDb

System Properties Comparison DataFS vs. HEAVY.AI vs. Microsoft Azure SQL Database vs. NSDb

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Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

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

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Primary database model

Object oriented DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Graph DBMS

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	76.78
Rank	#16	Overall
	#11	Relational DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Website

newdatabase.com

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

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

nsdb.io

Technical documentation

dev.mobiland.com/Overview.xsp

docs.heavy.ai

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

nsdb.io/Architecture

Developer

Mobiland AG

HEAVY.AI, Inc.

Microsoft

Initial release

2018

2016

2010

2017

Current release

1.1.263, October 2022

5.10, January 2022

V12

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

Apache Version 2.0

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++

Java, Scala

Server operating systems

Windows

Linux

hosted

Linux
macOS

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
ODBC

gRPC
HTTP REST
WebSocket

Supported programming languages

.Net
C
C#
C++
VB.Net

All languages supporting JDBC/ODBC/Thrift
Python

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

Java
Scala

Server-side scripts

Stored procedures

Transact SQL

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

Round robin

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes, with always 3 replicas available

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

Using Apache Lucene

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Windows-Profile

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

PostgreSQL is the DBMS of the Year 2020
4 January 2021, Paul Andlinger, Matthias Gelbmann

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

The popularity of cloud-based DBMSs has increased tenfold in four years
7 February 2017, Matthias Gelbmann

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Recent citations in the news

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16 February 2023, Datanami

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