DBMS > Snowflake vs. Teradata vs. TinkerGraph vs. UniData,UniVerse vs. Vertica

System Properties Comparison Snowflake vs. Teradata vs. TinkerGraph vs. UniData,UniVerse vs. Vertica

Editorial information provided by DB-Engines

Name

Description

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

A hybrid cloud data analytics software platform (Teradata Vantage)

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

MultiValue database and application server with SQL mapping layer and meta database capabilities

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Column oriented

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

Spatial DBMS
Time Series DBMS

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Trend Chart

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Score	0.13
Rank	#345	Overall
	#35	Graph DBMS

Score	2.85
Rank	#98	Overall
	#1	Multivalue DBMS

Score	10.06
Rank	#42	Overall
	#26	Relational DBMS

Website

www.snowflake.com

www.teradata.com

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-unidata

www.vertica.com

Technical documentation

docs.snowflake.net/manuals/index.html

docs.teradata.com

docs.rocketsoftware.com/bundle?cluster=true&labelkey=unidata&labelkey=prod_unidata

vertica.com/documentation

Developer

Snowflake Computing Inc.

Teradata

Rocket Software

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2014

1984

2009

1985

2005

Current release

Teradata Vantage 1.0 MU2, January 2019

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

hosted

hosted
Linux

AIX
HP-UX
Linux
Solaris
Windows

Linux

Data scheme

yes

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

yes

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

optional

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

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

SQL 2016 + extensions

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

CLI Client
JDBC
ODBC

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

TinkerPop 3

Java API

JPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

JavaScript (Node.js)
Python

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

Groovy
Java

.Net
Basic

U2 Basic
C
Java

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

yes

UDFs, stored procedures, table functions in parallel

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

similar concept for controling cloud resources

yes

U2 Basic

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

Hashing

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

none

Source-replica replication

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

configurable

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional

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

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

fine grained access rights according to SQL-standard

Access rights according to SQL-standard and operating system based

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
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