DBMS > Amazon Redshift vs. Apache Phoenix vs. RavenDB vs. Sadas Engine vs. Yanza

System Properties Comparison Amazon Redshift vs. Apache Phoenix vs. RavenDB vs. Sadas Engine vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Large scale data warehouse service for use with business intelligence tools

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

Open Source Operational and Transactional Enterprise NoSQL Document Database

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Time Series DBMS for IoT Applications

Primary database model

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

aws.amazon.com/redshift

phoenix.apache.org

ravendb.net

www.sadasengine.com

yanza.com

Technical documentation

docs.aws.amazon.com/redshift

phoenix.apache.org

ravendb.net/docs

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Hibernating Rhinos

SADAS s.r.l.

Yanza

Initial release

2012

2014

2010

2006

2015

Current release

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

5.4, July 2022

8.0

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

AGPL version 3, commercial license available

commercial

free trial version available

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Server operating systems

hosted

Linux
Unix
Windows

Linux
macOS
Raspberry Pi
Windows

AIX
Linux
Windows

Windows

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

yes

schema-free

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like query language (RQL)

yes

APIs and other access methods

JDBC
ODBC

JDBC

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

JDBC
ODBC
Proprietary protocol

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

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

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

.Net
C
C#
C++
Groovy
Java
PHP
Python

any language that supports HTTP calls

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

yes

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

ACID, Cluster-wide transaction available

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

managed by 'Learn by Usage'

User concepts

Access control

fine grained access rights according to SQL-standard

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

Authorization levels configured per client per database

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

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