DBMS > Amazon Redshift vs. Drizzle vs. HEAVY.AI vs. Transwarp Hippo

System Properties Comparison Amazon Redshift vs. Drizzle vs. HEAVY.AI vs. Transwarp Hippo

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Relational DBMS

Vector DBMS

Secondary database models

Spatial DBMS

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	0.00
Rank	#385	Overall
	#16	Vector DBMS

Website

aws.amazon.com/redshift

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

www.transwarp.cn/en/subproduct/hippo

Technical documentation

docs.aws.amazon.com/redshift

docs.heavy.ai

Developer

Amazon (based on PostgreSQL)

Drizzle project, originally started by Brian Aker

HEAVY.AI, Inc.

Initial release

2012

2008

2016

2023

Current release

7.2.4, September 2012

5.10, January 2022

1.0, May 2023

License

Commercial or Open Source

commercial

Open Source

GNU GPL

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

C++

C++ and CUDA

C++

Server operating systems

hosted

FreeBSD
Linux
OS X

Linux

Linux
macOS

Data scheme

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

yes

with proprietary extensions

yes

APIs and other access methods

JDBC
ODBC

JDBC

JDBC
ODBC
Thrift
Vega

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Java
PHP

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

Multi-source 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

User concepts

Access control

fine grained access rights according to SQL-standard

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

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