DBMS > HEAVY.AI vs. Microsoft Azure Table Storage vs. Snowflake vs. TigerGraph

System Properties Comparison HEAVY.AI vs. Microsoft Azure Table Storage vs. Snowflake vs. TigerGraph

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Editorial information provided by DB-Engines

Name

Description

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

A Wide Column Store for rapid development using massive semi-structured datasets

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

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Secondary database models

Spatial DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

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

azure.microsoft.com/en-us/services/storage/tables

www.snowflake.com

www.tigergraph.com

Technical documentation

docs.heavy.ai

docs.snowflake.net/manuals/index.html

docs.tigergraph.com

Developer

HEAVY.AI, Inc.

Microsoft

Snowflake Computing Inc.

Initial release

2016

2012

2014

2017

Current release

5.10, January 2022

License

Commercial or Open Source

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++ and CUDA

C++

Server operating systems

Linux

hosted

Linux

Data scheme

yes

schema-free

yes

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

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

SQL

Support of SQL

yes

SQL-like query language (GSQL)

APIs and other access methods

JDBC
ODBC
Thrift
Vega

RESTful HTTP API

CLI Client
JDBC
ODBC

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript (Node.js)
Python

C++
Java

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Implicit feature of the cloud service

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

optimistic locking

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

Access rights based on private key authentication or shared access signatures

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

Role-based access control

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