DBMS > Amazon Neptune vs. Microsoft SQL Server vs. Netezza vs. Quasardb vs. StarRocks

System Properties Comparison Amazon Neptune vs. Microsoft SQL Server vs. Netezza vs. Quasardb vs. StarRocks

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Microsofts flagship relational DBMS

Data warehouse and analytics appliance part of IBM PureSystems

Distributed, high-performance timeseries database

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	2.20
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	7.56
Rank	#48	Overall
	#31	Relational DBMS

Score	0.13
Rank	#332	Overall
	#28	Time Series DBMS

Score	0.95
Rank	#193	Overall
	#89	Relational DBMS

Website

aws.amazon.com/neptune

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

www.ibm.com/products/netezza

quasar.ai

www.starrocks.io

Technical documentation

aws.amazon.com/neptune/developer-resources

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

doc.quasar.ai/master

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

Amazon

Microsoft

IBM

quasardb

The Linux Foundation

since Feb 2023

Initial release

2017

1989

2000

2009

2020

Current release

SQL Server 2022, November 2022

3.14.1, January 2024

3.3, June 2024

License

Commercial or Open Source

commercial

restricted free version is available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

C++, Java

Server operating systems

hosted

Linux
Windows

Linux

included in appliance

BSD
Linux
OS X
Windows

Linux

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

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

JDBC
ODBC
OLE DB

HTTP API

JDBC
MySQL protocol

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

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

C
C++
Fortran
Java
Lua
Perl
Python
R

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Java

Server-side scripts

Stored procedures

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

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

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

Sharding

consistent hashing

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

yes, but depending on the SQL-Server Edition

Source-replica replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

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

with encyption-at-rest

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

Cryptographically strong user authentication and audit trail

Role based access control and fine grained access rights

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