DBMS > Amazon Redshift vs. Apache Druid vs. HEAVY.AI vs. Tibero

System Properties Comparison Amazon Redshift vs. Apache Druid vs. HEAVY.AI vs. Tibero

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Relational 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	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Website

aws.amazon.com/redshift

druid.apache.org

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

us.tmaxsoft.com/products/tibero

Technical documentation

docs.aws.amazon.com/redshift

druid.apache.org/docs/latest/design

docs.heavy.ai

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

HEAVY.AI, Inc.

TmaxSoft

Initial release

2012

2016

2003

Current release

29.0.1, April 2024

5.10, January 2022

6, April 2015

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

Apache Version 2; enterprise edition available

commercial

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

Java

C++ and CUDA

C and Assembler

Server operating systems

hosted

Linux
OS X
Unix

Linux

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-less columns are supported

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL for querying

yes

APIs and other access methods

JDBC
ODBC

JDBC
RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

All languages supporting JDBC/ODBC/Thrift
Python

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

user defined functions

in Python

Persistent Stored Procedure (PSM)

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

Round robin

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

Multi-source replication

Multi-source replication
Source-replica replication

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

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

planned for next version

User concepts

Access control

fine grained access rights according to SQL-standard

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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