DBMS > Amazon Redshift vs. Blueflood vs. FatDB vs. GridGain

System Properties Comparison Amazon Redshift vs. Blueflood vs. FatDB vs. GridGain

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

Large scale data warehouse service for use with business intelligence tools

Scalable TimeSeries DBMS based on Cassandra

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

GridGain is an in-memory computing platform, built on Apache Ignite

Primary database model

Relational DBMS

Time Series DBMS

Document store
Key-value store

Key-value store
Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Website

aws.amazon.com/redshift

blueflood.io

www.gridgain.com

Technical documentation

docs.aws.amazon.com/redshift

github.com/rax-maas/blueflood/wiki

www.gridgain.com/docs/index.html

Developer

Amazon (based on PostgreSQL)

Rackspace

FatCloud

GridGain Systems, Inc.

Initial release

2012

2013

2012

2007

Current release

GridGain 8.5.1

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

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

Java, C++, .Net

Server operating systems

hosted

Linux
OS X

Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

predefined scheme

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.

yes

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

Via inetgration in SQL Server

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

JDBC
ODBC

HTTP REST

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Java
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

via applications

yes (compute grid and cache interceptors can be used instead)

Triggers

yes

via applications

yes (cache interceptors and events)

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

selectable replication factor

yes (replicated cache)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

Immediate 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

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

Can implement custom security layer via applications

Security Hooks for custom implementations

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