DBMS > Amazon Redshift vs. Microsoft Azure SQL Database vs. OrientDB vs. Tkrzw vs. Vertica

System Properties Comparison Amazon Redshift vs. Microsoft Azure SQL Database vs. OrientDB vs. Tkrzw vs. Vertica

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

Multi-model DBMS (Document, Graph, Key/Value)

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store

Key-value store

Relational DBMS

Column oriented

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

aws.amazon.com/redshift

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

orientdb.org

dbmx.net/tkrzw

www.vertica.com

Technical documentation

docs.aws.amazon.com/redshift

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

www.orientdb.com/docs/last/index.html

vertica.com/documentation

Developer

Amazon (based on PostgreSQL)

Microsoft

OrientDB LTD; CallidusCloud; SAP

Mikio Hirabayashi

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2012

2010

2020

2005

Current release

V12

3.2.29, March 2024

0.9.3, August 2020

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Apache Version 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

C++

Server operating systems

hosted

All OS with a Java JDK (>= JDK 6)

Linux
macOS

Linux

Data scheme

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

restricted

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like query language, no joins

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

All languages supporting JDBC/ODBC

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

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

C++
Java
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in Python

Transact SQL

Java, Javascript

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

Hooks

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, with always 3 replicas available

Multi-source replication

none

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

relationship 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

using specific database classes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles; record level security configurable

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
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