DBMS > Amazon Redshift vs. Firebird vs. Kinetica vs. Microsoft Azure Table Storage vs. Spark SQL

System Properties Comparison Amazon Redshift vs. Firebird vs. Kinetica vs. Microsoft Azure Table Storage vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Firebird is an open source RDBMS forked from Borland's InterBase

Fully vectorized database across both GPUs and CPUs

A Wide Column Store for rapid development using massive semi-structured datasets

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Wide column store

Relational DBMS

Secondary database models

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	20.82
Rank	#30	Overall
	#18	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/redshift

www.firebirdsql.org

www.kinetica.com

azure.microsoft.com/en-us/services/storage/tables

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/redshift

www.firebirdsql.org/en/reference-manuals

docs.kinetica.com

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Amazon (based on PostgreSQL)

Firebird Foundation

Kinetica

Microsoft

Apache Software Foundation

Initial release

2012

2000

As fork of Borland's InterBase

2012

2014

Current release

5.0.0, January 2024

7.1, August 2021

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Initial Developer's Public License

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

C, C++

Scala

Server operating systems

hosted

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

Linux

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

PSQL

user defined functions

Triggers

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Features a multi-generational MVCC architecture, readers do not block writers

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

User concepts

Access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

Access rights for users and roles on table level

Access rights based on private key authentication or shared access signatures

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