DBMS > Amazon Redshift vs. BigchainDB vs. HEAVY.AI vs. SQream DB

System Properties Comparison Amazon Redshift vs. BigchainDB vs. HEAVY.AI vs. SQream DB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

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

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Relational DBMS

Document store

Relational 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	0.76
Rank	#216	Overall
	#36	Document stores

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	0.62
Rank	#234	Overall
	#109	Relational DBMS

Website

aws.amazon.com/redshift

www.bigchaindb.com

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

sqream.com

Technical documentation

docs.aws.amazon.com/redshift

bigchaindb.readthedocs.io/en/latest

docs.heavy.ai

docs.sqream.com

Developer

Amazon (based on PostgreSQL)

HEAVY.AI, Inc.

SQream Technologies

Initial release

2012

2016

2017

Current release

5.10, January 2022

2022.1.6, December 2022

License

Commercial or Open Source

commercial

Open Source

AGPL v3

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

Python

C++ and CUDA

C++, CUDA, Haskell, Java, Scala

Server operating systems

hosted

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

yes

APIs and other access methods

JDBC
ODBC

CLI Client
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

.Net
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Go
Haskell
Java
JavaScript
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Multi-source replication

none

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

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

yes,with MongoDB ord RethinkDB

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

yes

fine grained access rights according to SQL-standard

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