DBMS > Atos Standard Common Repository vs. BigchainDB vs. GridGain vs. KeyDB vs. Snowflake

System Properties Comparison Atos Standard Common Repository vs. BigchainDB vs. GridGain vs. KeyDB 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

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

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

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

Primary database model

Document store
Key-value store

Document store

Key-value store
Relational DBMS

Key-value store

Relational DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

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

www.bigchaindb.com

www.gridgain.com

github.com/Snapchat/KeyDB
keydb.dev

www.snowflake.com

Technical documentation

bigchaindb.readthedocs.io/en/latest

www.gridgain.com/docs/index.html

docs.keydb.dev

docs.snowflake.net/manuals/index.html

Developer

Atos Convergence Creators

GridGain Systems, Inc.

EQ Alpha Technology Ltd.

Snowflake Computing Inc.

Initial release

2016

2007

2019

2014

Current release

1703

GridGain 8.5.1

License

Commercial or Open Source

commercial

Open Source

AGPL v3

commercial

Open Source

BSD-3

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, C++, .Net

C++

Server operating systems

Linux

Linux
OS X
Solaris
Windows

Linux

hosted

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

schema-free

yes

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

Typing

predefined data types such as float or date

optional

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

SQL

Support of SQL

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

yes

APIs and other access methods

LDAP

CLI Client
RESTful HTTP API

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

Proprietary protocol

RESP - REdis Serialization Protoco

CLI Client
JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

Go
Haskell
Java
JavaScript
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

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

Lua

user defined functions

Triggers

yes

yes (cache interceptors and events)

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes (replicated cache)

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

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

Optimistic locking, atomic execution of commands blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

Security Hooks for custom implementations

simple password-based access control and ACL

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

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