DBMS > Amazon Redshift vs. Apache Impala vs. Faircom DB vs. Interbase vs. Newts

System Properties Comparison Amazon Redshift vs. Apache Impala vs. Faircom DB vs. Interbase vs. Newts

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Light-weight proven RDBMS

originally from Borland

Time Series DBMS based on Cassandra

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Document store

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	4.61
Rank	#72	Overall
	#39	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

aws.amazon.com/redshift

impala.apache.org

www.faircom.com/products/faircom-db

www.embarcadero.com/products/interbase

opennms.github.io/newts

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

docs.embarcadero.com/products/interbase

github.com/OpenNMS/newts/wiki

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

FairCom Corporation

Embarcadero

OpenNMS Group

Initial release

2012

2013

1979

1984

2014

Current release

4.1.0, June 2022

V12, November 2020

InterBase 2020, December 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Restricted, free version available

commercial

Open Source

Apache 2.0

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++

ANSI C, C++

Java

Server operating systems

hosted

Linux

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Android
iOS
Linux
OS X
Windows

Linux
OS X
Windows

Data scheme

yes

schema free, schema optional, schema required, partial schema,

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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.

export as XML data possible

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes, ANSI SQL with proprietary extensions

yes

APIs and other access methods

JDBC
ODBC

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

ADO.NET
JDBC
ODBC

HTTP REST
Java API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

Java

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

yes

.Net, JavaScript, C/C++

yes

Interbase procedure and trigger language

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

none

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Interbase Change Views

selectable replication factor

based on Cassandra

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

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

tunable from ACID to Eventually Consistent

ACID

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

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

Fine grained access rights according to SQL-standard with additional protections for files

fine grained access rights according to SQL-standard

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