DBMS > HEAVY.AI vs. Microsoft Azure AI Search vs. Snowflake vs. Teradata

System Properties Comparison HEAVY.AI vs. Microsoft Azure AI Search vs. Snowflake vs. Teradata

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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

Search-as-a-service for web and mobile app development

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

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Search engine

Relational DBMS

Secondary database models

Spatial DBMS

Vector DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

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

azure.microsoft.com/en-us/services/search

www.snowflake.com

www.teradata.com

Technical documentation

docs.heavy.ai

learn.microsoft.com/en-us/azure/search

docs.snowflake.net/manuals/index.html

docs.teradata.com

Developer

HEAVY.AI, Inc.

Microsoft

Snowflake Computing Inc.

Teradata

Initial release

2016

2015

2014

1984

Current release

5.10, January 2022

Teradata Vantage 1.0 MU2, January 2019

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

Server operating systems

Linux

hosted

hosted
Linux

Data scheme

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

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

SQL 2016 + extensions

APIs and other access methods

JDBC
ODBC
Thrift
Vega

RESTful HTTP API

CLI Client
JDBC
ODBC

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C#
Java
JavaScript
Python

JavaScript (Node.js)
Python

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions

yes

UDFs, stored procedures, table functions in parallel

Triggers

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Implicit feature of the cloud service

yes

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Implicit feature of the cloud service

yes

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

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

yes

using Azure authentication

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

fine grained access rights according to SQL-standard

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