DBMS > Amazon Aurora vs. Apache Impala vs. Blazegraph vs. Drizzle vs. Microsoft SQL Server

System Properties Comparison Amazon Aurora vs. Apache Impala vs. Blazegraph vs. Drizzle vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Analytic DBMS for Hadoop

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Microsofts flagship relational DBMS

Primary database model

Relational DBMS

Graph DBMS
RDF store

Relational DBMS

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

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

Score	7.91
Rank	#50	Overall
	#32	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.75
Rank	#219	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

aws.amazon.com/rds/aurora

impala.apache.org

blazegraph.com

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

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

impala.apache.org/impala-docs.html

wiki.blazegraph.com

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

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Blazegraph

Drizzle project, originally started by Brian Aker

Microsoft

Initial release

2015

2013

2006

2008

1989

Current release

4.1.0, June 2022

2.1.5, March 2019

7.2.4, September 2012

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

extended commercial license available

Open Source

GNU GPL

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Java

C++

Server operating systems

hosted

Linux

Linux
OS X
Windows

FreeBSD
Linux
OS X

Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

RDF literal types

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 DML and DDL statements

SPARQL is used as query language

yes

with proprietary extensions

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

JDBC

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

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

All languages supporting JDBC/ODBC

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

C
C++
Java
PHP

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

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

Triggers

yes

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

yes

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Security and Authentication via Web Application Container (Tomcat, Jetty)

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

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