DBMS > Amazon Redshift vs. Hawkular Metrics vs. HBase vs. MarkLogic vs. Warp 10

System Properties Comparison Amazon Redshift vs. Hawkular Metrics vs. HBase vs. MarkLogic vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

Operational and transactional Enterprise NoSQL database

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Relational DBMS

Time Series DBMS

Wide column store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Time Series DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

aws.amazon.com/redshift

www.hawkular.org

hbase.apache.org

www.progress.com/marklogic

www.warp10.io

Technical documentation

docs.aws.amazon.com/redshift

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

hbase.apache.org/book.html

www.progress.com/marklogic/documentation

www.warp10.io/content/02_Getting_started

Developer

Amazon (based on PostgreSQL)

Community supported by Red Hat

Apache Software Foundation

Apache top-level project, originally developed by Powerset

MarkLogic Corp.

SenX

Initial release

2012

2014

2008

2001

2015

Current release

2.3.4, January 2021

11.0, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache version 2

commercial

restricted free version is available

Open Source

Apache License 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

Java

C++

Java

Server operating systems

hosted

Linux
OS X
Windows

Linux
Unix
Windows

using Cygwin

Linux
OS X
Windows

Data scheme

yes

schema-free

schema-free, schema definition possible

schema-free

Schema can be enforced

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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.

yes

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL92

APIs and other access methods

JDBC
ODBC

HTTP REST

Java API
RESTful HTTP API
Thrift

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

HTTP API
Jupyter
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Coprocessors in Java

yes

via XQuery or JavaScript

yes

WarpScript

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency or Eventual Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

ACID

can act as a resource manager in an XA/JTA transaction

Concurrency

Support for concurrent manipulation of data

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

yes, with Range Indexes

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Role-based access control at the document and subdocument levels

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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