DBMS > Apache IoTDB vs. EXASOL vs. FatDB vs. Splunk

System Properties Comparison Apache IoTDB vs. EXASOL vs. FatDB vs. Splunk

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the 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

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Analytics Platform for Big Data

Primary database model

Time Series DBMS

Relational DBMS

Document store
Key-value store

Search engine

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	1.66
Rank	#138	Overall
	#64	Relational DBMS

Score	93.02
Rank	#13	Overall
	#2	Search engines

Website

iotdb.apache.org

www.exasol.com

www.splunk.com

Technical documentation

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

www.exasol.com/resources

docs.splunk.com/Documentation/Splunk

Developer

Apache Software Foundation

Exasol

FatCloud

Splunk Inc.

Initial release

2018

2000

2012

2003

Current release

1.1.0, April 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Limited free edition and free developer edition available

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

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

Via inetgration in SQL Server

Splunk Search Processing Language for search commands

APIs and other access methods

JDBC
Native API

.Net
JDBC
ODBC
WebSocket

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

HTTP REST

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java
Lua
Python
R

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

yes

via applications

yes

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

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

selectable replication factor

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

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

User concepts

Access control

yes

Access rights for users, groups and roles according to SQL-standard

Can implement custom security layer via applications

Access rights for users and roles

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