DBMS > BigObject vs. Hawkular Metrics vs. OushuDB vs. Sadas Engine

System Properties Comparison BigObject vs. Hawkular Metrics vs. OushuDB vs. Sadas Engine

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

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

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Time Series DBMS

Relational DBMS

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	0.10
Rank	#355	Overall
	#154	Relational DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

bigobject.io

www.hawkular.org

www.oushu.com/product/oushuDB

www.sadasengine.com

Technical documentation

docs.bigobject.io

www.hawkular.org/hawkular-metrics/docs/user-guide

www.oushu.com/documentation

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

Developer

BigObject, Inc.

Community supported by Red Hat

Oushu

SADAS s.r.l.

Initial release

2015

2014

2006

Current release

4.0.1, August 2020

8.0

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

commercial

free trial version available

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++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
OS X
Windows

Linux

AIX
Linux
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

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Full-featured ANSI SQL support

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

HTTP REST

JDBC
ODBC

JDBC
ODBC
Proprietary protocol

Supported programming languages

Go
Java
Python
Ruby

C
C++

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

Server-side scripts

Stored procedures

Lua

yes

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

yes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

Kerberos, SSL and role based access

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

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