DBMS > Amazon Redshift vs. Apache IoTDB vs. Datastax Enterprise vs. RDF4J

System Properties Comparison Amazon Redshift vs. Apache IoTDB vs. Datastax Enterprise vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	5.80
Rank	#60	Overall
	#4	Wide column stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

aws.amazon.com/redshift

iotdb.apache.org

www.datastax.com/products/datastax-enterprise

rdf4j.org

Technical documentation

docs.aws.amazon.com/redshift

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

docs.datastax.com

rdf4j.org/documentation

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

DataStax

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2012

2018

2011

2004

Current release

1.1.0, April 2023

6.8, April 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Datastax Astra DB: Astra DB simplifies cloud-native Cassandra application development for your apps, microservices and functions. Deploy in minutes on AWS, Google Cloud, Azure, and have it managed for you by the experts, with serverless, pay-as-you-go pricing.

Implementation language

Java

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

Linux
OS X

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

SQL-like DML and DDL statements (CQL); Spark SQL

APIs and other access methods

JDBC
ODBC

JDBC
Native API

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

configurable replication factor, datacenter aware, advanced replication for edge computing

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

yes

Access rights for users can be defined per object

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
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Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
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Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
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Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
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Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
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Licensing and pricing models

Annual subscription
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