DBMS > Apache IoTDB vs. Brytlyt vs. EXASOL

System Properties Comparison Apache IoTDB vs. Brytlyt vs. EXASOL

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Editorial information provided by DB-Engines

Name

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

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

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

Primary database model

Time Series DBMS

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	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Website

iotdb.apache.org

brytlyt.io

www.exasol.com

Technical documentation

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

docs.brytlyt.io

www.exasol.com/resources

Developer

Apache Software Foundation

Brytlyt

Exasol

Initial release

2018

2016

2000

Current release

1.1.0, April 2023

5.0, August 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, C++ and CUDA

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X
Windows

Data 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.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

.Net
JDBC
ODBC
WebSocket

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

Java
Lua
Python
R

Server-side scripts

Stored procedures

yes

user defined functions

in PL/pgSQL

user defined functions

Triggers

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

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

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

fine grained access rights according to SQL-standard

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

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