DBMS > BigchainDB vs. IBM Db2 Event Store vs. SiteWhere vs. Yaacomo

System Properties Comparison BigchainDB vs. IBM Db2 Event Store vs. SiteWhere vs. Yaacomo

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Editorial information provided by DB-Engines

Name

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

Description

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

Distributed Event Store optimized for Internet of Things use cases

M2M integration platform for persisting/querying time series data

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

Primary database model

Document store

Event Store
Time Series DBMS

Time Series DBMS

Relational DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

www.bigchaindb.com

www.ibm.com/products/db2-event-store

github.com/sitewhere/sitewhere

yaacomo.com

Technical documentation

bigchaindb.readthedocs.io/en/latest

www.ibm.com/docs/en/db2-event-store

sitewhere1.sitewhere.io/index.html

Developer

IBM

SiteWhere

Q2WEB GmbH

Initial release

2016

2017

2010

2009

Current release

2.0

License

Commercial or Open Source

Open Source

AGPL v3

commercial

free developer edition available

Open Source

Common Public Attribution License Version 1.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Python

C and C++

Java

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux
OS X
Windows

Android
Linux
Windows

Data scheme

schema-free

yes

predefined scheme

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

APIs and other access methods

CLI Client
RESTful HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HTTP REST

JDBC
ODBC

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on HBase

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Active-active shard replication

selectable replication factor

based on HBase

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard

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More resources

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