DBMS > Amazon Redshift vs. FeatureBase vs. HEAVY.AI vs. SiteWhere

System Properties Comparison Amazon Redshift vs. FeatureBase vs. HEAVY.AI vs. SiteWhere

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.22
Rank	#309	Overall
	#139	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

aws.amazon.com/redshift

www.featurebase.com

github.com/heavyai/heavydb
www.heavy.ai

github.com/sitewhere/sitewhere

Technical documentation

docs.aws.amazon.com/redshift

docs.featurebase.com

docs.heavy.ai

sitewhere1.sitewhere.io/index.html

Developer

Amazon (based on PostgreSQL)

Molecula and Pilosa Open Source Contributors

HEAVY.AI, Inc.

SiteWhere

Initial release

2012

2017

2016

2010

Current release

2022, May 2022

5.10, January 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Common Public Attribution License Version 1.0

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.

Implementation language

C++ and CUDA

Java

Server operating systems

hosted

Linux
macOS

Linux

Linux
OS X
Windows

Data scheme

yes

predefined scheme

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

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL queries

yes

APIs and other access methods

JDBC
ODBC

gRPC
JDBC
Kafka Connector
ODBC

JDBC
ODBC
Thrift
Vega

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC

Java
Python

All languages supporting JDBC/ODBC/Thrift
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using Linux fsync

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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