DBMS > BigchainDB vs. HEAVY.AI vs. Snowflake vs. SQream DB

System Properties Comparison BigchainDB vs. HEAVY.AI vs. Snowflake vs. SQream DB

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Editorial information provided by DB-Engines

Name

Description

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

Cloud-based data warehousing service for structured and semi-structured data

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

Primary database model

Document store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.76
Rank	#216	Overall
	#36	Document stores

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	0.62
Rank	#234	Overall
	#109	Relational DBMS

Website

www.bigchaindb.com

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

www.snowflake.com

sqream.com

Technical documentation

bigchaindb.readthedocs.io/en/latest

docs.heavy.ai

docs.snowflake.net/manuals/index.html

docs.sqream.com

Developer

HEAVY.AI, Inc.

Snowflake Computing Inc.

SQream Technologies

Initial release

2016

2014

2017

Current release

5.10, January 2022

2022.1.6, December 2022

License

Commercial or Open Source

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

Linux

hosted

Linux

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

Secondary indexes

SQL

Support of SQL

yes

APIs and other access methods

CLI Client
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

CLI Client
JDBC
ODBC

.Net
JDBC
ODBC

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript (Node.js)
Python

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

user defined functions in Python

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

yes

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

yes

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

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,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

yes

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

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