DBMS > Amazon Neptune vs. HyperSQL vs. KeyDB vs. Microsoft SQL Server vs. Netezza

System Properties Comparison Amazon Neptune vs. HyperSQL vs. KeyDB vs. Microsoft SQL Server vs. Netezza

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Microsofts flagship relational DBMS

Data warehouse and analytics appliance part of IBM PureSystems

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	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Website

aws.amazon.com/neptune

hsqldb.org

github.com/Snapchat/KeyDB
keydb.dev

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

www.ibm.com/products/netezza

Technical documentation

aws.amazon.com/neptune/developer-resources

hsqldb.org/web/hsqlDocsFrame.html

docs.keydb.dev

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

Developer

Amazon

EQ Alpha Technology Ltd.

Microsoft

IBM

Initial release

2017

2001

2019

1989

2000

Current release

2.7.2, June 2023

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

based on BSD license

Open Source

BSD-3

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

Java

C++

Server operating systems

hosted

All OS with a Java VM

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

Linux

Linux
Windows

Linux

included in appliance

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

yes

SQL

Support of SQL

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

HTTP API

JDBC via HTTP
JDBC
ODBC

Proprietary protocol

RESP - REdis Serialization Protoco

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

JDBC
ODBC
OLE DB

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC
Java

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

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

C
C++
Fortran
Java
Lua
Perl
Python
R

Server-side scripts

Stored procedures

Java, SQL

Lua

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

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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

Sharding

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.

none

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

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

Optimistic locking, atomic execution of commands blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

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

simple password-based access control and ACL

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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