DBMS > Amazon Redshift vs. Blueflood vs. HBase

System Properties Comparison Amazon Redshift vs. Blueflood vs. HBase

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Scalable TimeSeries DBMS based on Cassandra

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

Primary database model

Relational DBMS

Time Series DBMS

Wide column store

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	0.09
Rank	#353	Overall
	#33	Time Series DBMS

Score	31.25
Rank	#26	Overall
	#2	Wide column stores

Website

aws.amazon.com/redshift

blueflood.io

hbase.apache.org

Technical documentation

docs.aws.amazon.com/redshift

github.com/rax-maas/blueflood/wiki

hbase.apache.org/book.html

Developer

Amazon (based on PostgreSQL)

Rackspace

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Initial release

2012

2013

2008

Current release

2.3.4, January 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

Linux
OS X

Linux
Unix
Windows

using Cygwin

Data scheme

yes

predefined scheme

schema-free, schema definition possible

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

SQL

Support of SQL

yes

does not fully support an SQL-standard

APIs and other access methods

JDBC
ODBC

HTTP REST

Java API
RESTful HTTP API
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Groovy
Java
PHP
Python
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Coprocessors in Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

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)

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

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

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