DBMS > Atos Standard Common Repository vs. etcd vs. Google BigQuery vs. Percona Server for MongoDB vs. VelocityDB

System Properties Comparison Atos Standard Common Repository vs. etcd vs. Google BigQuery vs. Percona Server for MongoDB vs. VelocityDB

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

A distributed reliable key-value store

Large scale data warehouse service with append-only tables

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Document store
Key-value store

Key-value store

Relational DBMS

Document store

Graph DBMS
Object oriented DBMS

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

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

Score	0.05
Rank	#358	Overall
	#36	Graph DBMS
	#16	Object oriented DBMS

Website

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

etcd.io
github.com/etcd-io/etcd

cloud.google.com/bigquery

www.percona.com/mongodb/software/percona-server-for-mongodb

velocitydb.com

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

cloud.google.com/bigquery/docs

docs.percona.com/percona-distribution-for-mongodb

velocitydb.com/UserGuide

Developer

Atos Convergence Creators

Google

Percona

VelocityDB Inc

Initial release

2016

2010

2015

2011

Current release

1703

3.4, August 2019

3.4.10-2.10, November 2017

7.x

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

GPL Version 2

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

C++

Server operating systems

Linux

FreeBSD
Linux
Windows

experimental

hosted

Linux

Any that supports .NET

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

schema-free

yes

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

SQL

Support of SQL

yes

APIs and other access methods

LDAP

gRPC
JSON over HTTP

RESTful HTTP/JSON API

proprietary protocol using JSON

.Net

Supported programming languages

All languages with LDAP bindings

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

.Net

Server-side scripts

Stored procedures

user defined functions

in JavaScript

JavaScript

Triggers

yes

yes, watching key changes

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

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 or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

Since BigQuery is designed for querying 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

via In-Memory Engine

yes

User concepts

Access control

LDAP bind authentication

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles

Based on Windows Authentication

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