DBMS > Apache IoTDB vs. Atos Standard Common Repository vs. BigchainDB vs. KeyDB vs. Snowflake

System Properties Comparison Apache IoTDB vs. Atos Standard Common Repository vs. BigchainDB 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

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

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

Time Series DBMS

Document store
Key-value store

Document store

Key-value store

Relational DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.79
Rank	#212	Overall
	#36	Document stores

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

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

iotdb.apache.org

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

www.bigchaindb.com

github.com/Snapchat/KeyDB
keydb.dev

www.snowflake.com

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

bigchaindb.readthedocs.io/en/latest

docs.keydb.dev

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Atos Convergence Creators

EQ Alpha Technology Ltd.

Snowflake Computing Inc.

Initial release

2018

2016

2019

2014

Current release

1.1.0, April 2023

1703

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

AGPL v3

Open Source

BSD-3

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Python

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

hosted

Data scheme

yes

Schema and schema-less with LDAP views

schema-free

yes

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

Typing

predefined data types such as float or date

yes

optional

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

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

LDAP

CLI Client
RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protoco

CLI Client
JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

All languages with LDAP bindings

Go
Haskell
Java
JavaScript
Python
Ruby

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

Lua

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

cell division

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

selectable replication factor

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency or Eventual Consistency depending on configuration

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

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

yes

LDAP bind authentication

yes

simple password-based access control and ACL

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

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