DBMS > DuckDB vs. IBM Db2 Event Store vs. SiteWhere vs. Spark SQL

System Properties Comparison DuckDB vs. IBM Db2 Event Store vs. SiteWhere vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

An embeddable, in-process, column-oriented SQL OLAP RDBMS

Distributed Event Store optimized for Internet of Things use cases

M2M integration platform for persisting/querying time series data

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Time Series DBMS

Relational DBMS

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

Score	4.63
Rank	#69	Overall
	#37	Relational DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

duckdb.org

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

github.com/sitewhere/sitewhere

spark.apache.org/sql

Technical documentation

duckdb.org/docs

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

sitewhere1.sitewhere.io/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

IBM

SiteWhere

Apache Software Foundation

Initial release

2018

2017

2010

2014

Current release

1.0.0, June 2024

2.0

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

MIT License

commercial

free developer edition available

Open Source

Common Public Attribution License Version 1.0

Open Source

Apache 2.0

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

C++

C and C++

Java

Scala

Server operating systems

server-less

Linux

Linux, macOS, Windows for the developer addition

Linux
OS X
Windows

Data scheme

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

SQL-like DML and DDL statements

APIs and other access methods

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HTTP REST

JDBC
ODBC

Supported programming languages

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on HBase

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Active-active shard replication

selectable replication factor

based on HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

No - written data is immutable

yes

Durability

Support for making data persistent

yes

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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More resources

Recent citations in the news

DuckDB promises greater stability with 1.0 release
5 June 2024, The Register

DuckDB 1.0 Released
4 June 2024, iProgrammer

DuckDB Walks to the Beat of Its Own Analytics Drum
5 March 2024, Datanami

DuckDB: In-Process Python Analytics for Not-Quite-Big Data
31 May 2024, The New Stack

My First Billion (of Rows) in DuckDB | by João Pedro | May, 2024
1 May 2024, Towards Data Science

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25 July 2019, IBM

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3 June 2019, Datanami

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SiteWhere: An open platform for connected devices
11 July 2017, Open Source For You

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27 March 2023, Open Source For You

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9 March 2023, H2S Media

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1 June 2024, Towards Data Science

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