DBMS > FatDB vs. Heroic vs. Netezza vs. Tkrzw vs. Yaacomo

System Properties Comparison FatDB vs. Heroic vs. Netezza vs. Tkrzw vs. Yaacomo

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.

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Data warehouse and analytics appliance part of IBM PureSystems

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Document store
Key-value store

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

github.com/spotify/heroic

www.ibm.com/products/netezza

dbmx.net/tkrzw

yaacomo.com

Technical documentation

spotify.github.io/heroic

Developer

FatCloud

Spotify

IBM

Mikio Hirabayashi

Q2WEB GmbH

Initial release

2012

2014

2000

2020

2009

Current release

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0

commercial

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

Windows

Linux

included in appliance

Linux
macOS

Android
Linux
Windows

Data scheme

schema-free

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

via Elasticsearch

yes

SQL

Support of SQL

Via inetgration in SQL Server

yes

APIs and other access methods

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

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC
OLE DB

JDBC
ODBC

Supported programming languages

C
C++
Fortran
Java
Lua
Perl
Python
R

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

via applications

yes

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Source-replica replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

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

using specific database classes

yes

User concepts

Access control

Can implement custom security layer via applications

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard

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More resources

Recent citations in the news

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7 September 2016, InfoWorld

provided by Google News

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Migrating your Netezza data warehouse to Amazon Redshift | Amazon Web Services
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