DBMS > Amazon Redshift vs. Kinetica vs. Raima Database Manager vs. Solr

System Properties Comparison Amazon Redshift vs. Kinetica vs. Raima Database Manager vs. Solr

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Fully vectorized database across both GPUs and CPUs

RDBMS with a small footprint, either embedded into an application or used as a database server

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Search engine

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	0.69
Rank	#234	Overall
	#107	Relational DBMS

Score	0.48
Rank	#261	Overall
	#122	Relational DBMS
	#22	Time Series DBMS

Score	44.28
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/redshift

www.kinetica.com

raima.com

solr.apache.org

Technical documentation

docs.aws.amazon.com/redshift

docs.kinetica.com

docs.raima.com

solr.apache.org/resources.html

Developer

Amazon (based on PostgreSQL)

Kinetica

Raima Inc.

Apache Software Foundation

Initial release

2012

1984

2006

Current release

7.1, August 2021

15, June 2021

9.5.0, February 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, C++

Java

Server operating systems

hosted

Linux

Android
HP-UX
Integrity RTOS
iOS
Linux
OS X
QNX
Solaris
VxWorks
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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.

Exporting/Importing of XML structures possible

yes

Secondary indexes

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
RESTful HTTP API

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
JavaScript (Node.js)
Python

.Net
C
C++
Objective-C
PL/SQL

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

yes

based on the ANSI/ISO SQL Persistent Stored Modules (PSM) specification

Java plugins

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Eventual 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

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

GPU vRAM or System RAM

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

yes

More information provided by the system vendor

Specific characteristics

Raima Database Manager (RDM) is an embedded time-series database optimized to run...
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Competitive advantages

Decreased time-to-market Industry-standard interfaces & tools for making your job...
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Typical application scenarios

Typical use cases include: Internet of Things (IoT) Embedded In-Memory Database Time-Series...
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Key customers

Infor, ABB, The Boeing Company, BAE Systems, Dow Jones, Dun & Bradstreet, Fujitsu,...
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Market metrics

Over 30 000 applications developers worldwide have field-tested RDM products More...
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Licensing and pricing models

Just as the technical aspects of Raima Database Manager are flexible, so are the...
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News

Raima Unveils RaimaDB 16.0: Elevating Database Performance and User Experience
29 February 2024

Raima Announces Release of Raima Database Manager (RDM) v15, a Powerful Embedded Time Series Database
10 June 2021

Raima Announces Release of Raima Database Manager (RDM) v14.2, a Powerful IoT Edge Embedded Database
8 October 2020

Raima Joins BlackBerry QNX Channel Partner Program to Deliver Safety-Critical and Secure Software Solutions
1 October 2020

Raima Expands Their Reseller Network in Asia
19 May 2020

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.