DBMS > Atos Standard Common Repository vs. BigchainDB vs. Blueflood vs. Riak KV vs. Snowflake

System Properties Comparison Atos Standard Common Repository vs. BigchainDB vs. Blueflood vs. Riak KV vs. Snowflake

Editorial information provided by DB-Engines

Name

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

Description

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

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Scalable TimeSeries DBMS based on Cassandra

Distributed, fault tolerant key-value store

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Document store
Key-value store

Document store

Time Series DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

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

Score	0.82
Rank	#212	Overall
	#36	Document stores

Score	0.09
Rank	#353	Overall
	#33	Time Series DBMS

Score	4.44
Rank	#80	Overall
	#9	Key-value stores

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Website

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

www.bigchaindb.com

blueflood.io

www.snowflake.com

Technical documentation

bigchaindb.readthedocs.io/en/latest

github.com/rax-maas/blueflood/wiki

www.tiot.jp/riak-docs/riak/kv/latest

docs.snowflake.net/manuals/index.html

Developer

Atos Convergence Creators

Rackspace

OpenSource, formerly Basho Technologies

Snowflake Computing Inc.

Initial release

2016

2013

2009

2014

Current release

1703

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

AGPL v3

Open Source

Apache 2.0

Open Source

Apache version 2, commercial enterprise edition

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

Python

Java

Erlang

Server operating systems

Linux

Linux
OS X

hosted

Data scheme

Schema and schema-less with LDAP views

schema-free

predefined scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

optional

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

restricted

SQL

Support of SQL

yes

APIs and other access methods

LDAP

CLI Client
RESTful HTTP API

HTTP REST

HTTP API
Native Erlang Interface

CLI Client
JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

Go
Haskell
Java
JavaScript
Python
Ruby

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Erlang

user defined functions

Triggers

yes

pre-commit hooks and post-commit hooks

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

based on Cassandra

Sharding

no "single point of failure"

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

links between data sets can be stored

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

yes,with MongoDB ord RethinkDB

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

yes

yes, using Riak Security

Users with fine-grained authorization concept, user roles and pluggable authentication

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