DBMS > Atos Standard Common Repository vs. Blueflood vs. NSDb vs. Yaacomo vs. Yanza

System Properties Comparison Atos Standard Common Repository vs. Blueflood vs. NSDb vs. Yaacomo vs. Yanza

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

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

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Scalable TimeSeries DBMS based on Cassandra

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

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

Time Series DBMS for IoT Applications

Primary database model

Document store
Key-value store

Time Series DBMS

Relational DBMS

Time Series DBMS

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

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

blueflood.io

nsdb.io

yaacomo.com

yanza.com

Technical documentation

github.com/rax-maas/blueflood/wiki

nsdb.io/Architecture

Developer

Atos Convergence Creators

Rackspace

Q2WEB GmbH

Yanza

Initial release

2016

2013

2017

2009

2015

Current release

1703

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Java, Scala

Server operating systems

Linux

Linux
OS X

Linux
macOS

Android
Linux
Windows

Windows

Data scheme

Schema and schema-less with LDAP views

predefined scheme

yes

schema-free

Typing

predefined data types such as float or date

optional

yes

yes: int, bigint, decimal, string

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

yes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

LDAP

HTTP REST

gRPC
HTTP REST
WebSocket

JDBC
ODBC

HTTP API

Supported programming languages

All languages with LDAP bindings

Java
Scala

any language that supports HTTP calls

Server-side scripts

Stored procedures

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

based on Cassandra

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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

LDAP bind authentication

fine grained access rights according to SQL-standard

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