DBMS > Axibase vs. Bangdb vs. Faircom DB vs. Netezza vs. Prometheus

System Properties Comparison Axibase vs. Bangdb vs. Faircom DB vs. Netezza vs. Prometheus

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Converged and high performance database for device data, events, time series, document and graph

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

Data warehouse and analytics appliance part of IBM PureSystems

Open-source Time Series DBMS and monitoring system

Primary database model

Time Series DBMS

Document store
Graph DBMS
Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

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

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Website

axibase.com/docs/atsd/finance

bangdb.com

www.faircom.com/products/faircom-db

www.ibm.com/products/netezza

prometheus.io

Technical documentation

docs.bangdb.com

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

prometheus.io/docs

Developer

Axibase Corporation

Sachin Sinha, BangDB

FairCom Corporation

IBM

Initial release

2013

2012

1979

2000

2015

Current release

15585

BangDB 2.0, October 2021

V12, November 2020

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

Open Source

BSD 3

commercial

Restricted, free version available

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C, C++

ANSI C, C++

Server operating systems

Linux

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

easily portable to other OSs

Linux

included in appliance

Linux
Windows

Data scheme

yes

schema-free

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

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes: string, long, double, int, geospatial, stream, events

yes, ANSI SQL Types, JSON, typed binary structures

yes

Numeric data only

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.

Import of XML data possible

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL-like query language

SQL like support with command line tool

yes, ANSI SQL with proprietary extensions

yes

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

Proprietary protocol
RESTful HTTP API

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

JDBC
ODBC
OLE DB

RESTful HTTP/JSON API

Supported programming languages

Go
Java
PHP
Python
R
Ruby

C
C#
C++
Java
Python

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

C
C++
Fortran
Java
Lua
Perl
Python
R

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Triggers

yes

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor, Knob for CAP (enterprise version only)

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

Source-replica replication

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

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

none

Foreign keys

Referential integrity

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

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

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, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

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

Users with fine-grained authorization concept

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