DBMS > VelocityDB vs. Vertica vs. Yaacomo

System Properties Comparison VelocityDB vs. Vertica vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Description

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Cloud or on-premises 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.

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Graph DBMS
Object oriented DBMS

Relational DBMS

Column oriented

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.03
Rank	#336	Overall
	#35	Graph DBMS
	#17	Object oriented DBMS

Score	19.29
Rank	#38	Overall
	#24	Relational DBMS

Score	0.00
Rank	#354	Overall
	#152	Relational DBMS

Website

velocitydb.com

www.vertica.com

yaacomo.com

Technical documentation

velocitydb.com/UserGuide

vertica.com/documentation

Developer

VelocityDB Inc

Micro Focus

prior to that Hewlett Packard

Q2WEB GmbH

Initial release

2011

2005

2009

Current release

7.x

11.1, February 2022

License

Commercial or Open Source

commercial

Limited community edition free

commercial

Cloud-based only

Only available as a cloud service

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.

Vertica Accelerator’s high-performance analytics and machine learning with SQL or Python is available as a managed service.

Implementation language

C++

Server operating systems

Any that supports .NET

Linux

Android
Linux
Windows

Data scheme

yes

yes, but unstructured data can be stored in specific Flex-Tables

yes

Typing

predefined data types such as float or date

yes

yes: BINARY, BOOLEAN, CHAR, VARCHAR, LONG VARCHAR, DATE, TIME, TIMESTAMP, INTERVAL, INTERVAL DAY TO SECOND, INTERVAL YEAR TO MONTH, DOUBLE PRECISION, FLOAT, INTEGER, BIGINT, SMALLINT,NUMERIC, DECIMAL, NUMBER, MONEY,GEOMETRY

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.

Secondary indexes

yes

No Indexes Required

yes

SQL

Support of SQL

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

yes

APIs and other access methods

.Net

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

character-based, interactive, front-end utility

JDBC
ODBC

Supported programming languages

.Net

C#
C++
Java
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

Callbacks are triggered when data changes

yes, called Custom Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, hierarchical partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

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

Source-replica replication

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

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

Based on Windows Authentication

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy on-premises,...
» more

Competitive advantages

Fast, scalable and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

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

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More resources

Recent citations in the news

VAST links arms with Vertica for fast analytics – Blocks and Files
19 April 2022, Blocks and Files

“Above the Trend Line” – Your Industry Rumor Central for 5/5/2022
5 May 2022, insideBIGDATA

Sesame Software Launches Vertica Data Connector for High-Volume Connectivity to Cloud and On-Premise Applications
15 February 2022, PR Newswire

Vertica and H3C ONEStor Bring Cloud-Scale Analytics to Enterprise Data Centers
23 February 2022, PR Newswire

Vertica and H3C ONEStor Unite to Simplify Database Operations
28 February 2022, Read IT Quik

provided by Google News

Job opportunities

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