DBMS > Amazon Neptune vs. Apache Phoenix vs. Derby vs. EXASOL vs. Microsoft SQL Server

System Properties Comparison Amazon Neptune vs. Apache Phoenix vs. Derby vs. EXASOL vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

A scale-out RDBMS with evolutionary schema built on Apache HBase

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Microsofts flagship relational DBMS

Primary database model

Graph DBMS
RDF store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	2.29
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	4.60
Rank	#70	Overall
	#38	Relational DBMS

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Website

aws.amazon.com/neptune

phoenix.apache.org

db.apache.org/derby

www.exasol.com

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

Technical documentation

aws.amazon.com/neptune/developer-resources

phoenix.apache.org

db.apache.org/derby/manuals/index.html

www.exasol.com/resources

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

Developer

Amazon

Apache Software Foundation

Exasol

Microsoft

Initial release

2017

2014

1997

2000

1989

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

10.17.1.0, November 2023

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache version 2

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Server operating systems

hosted

Linux
Unix
Windows

All OS with a Java VM

Linux
Windows

Data scheme

schema-free

yes

late-bound, schema-on-read capabilities

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

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JDBC

.Net
JDBC
ODBC
WebSocket

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

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

Java

Java
Lua
Python
R

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

Server-side scripts

Stored procedures

user defined functions

Java Stored Procedures

user defined functions

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

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

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.

Multi-source replication
Source-replica replication

Source-replica replication

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

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

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)

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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