DBMS > TigerGraph vs. Vertica vs. Yaacomo

System Properties Comparison TigerGraph vs. Vertica vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

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.

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

Primary database model

Graph 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	1.83
Rank	#141	Overall
	#13	Graph DBMS

Score	11.40
Rank	#43	Overall
	#27	Relational DBMS

Website

www.tigergraph.com

www.vertica.com

yaacomo.com

Technical documentation

docs.tigergraph.com

vertica.com/documentation

Developer

OpenText

previously Micro Focus and Hewlett Packard

Q2WEB GmbH

Initial release

2017

2005

2009

Current release

12.0.3, January 2023

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.

Implementation language

C++

Server operating systems

Linux

Android
Linux
Windows

Data scheme

yes

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

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.

Secondary indexes

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

yes

SQL

Support of SQL

SQL-like query language (GSQL)

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

yes

APIs and other access methods

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

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

character-based, interactive, front-end utility

JDBC
ODBC

Supported programming languages

C++
Java

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

Server-side scripts

Stored procedures

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes, called Custom Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

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

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

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

Role-based access control

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 off-cloud,...
» 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

New TigerGraph CEO Refocuses Efforts on Enterprise Customers
31 July 2023, Datanami

TigerGraph update adds enterprise-scale capabilities
31 October 2023, TechTarget

TigerGraph Bolsters DB for Enterprise Graph Workloads
1 November 2023, Datanami

Aerospike takes on Neo4j and TigerGraph with launch of graph database
20 June 2023, SiliconANGLE News

TigerGraph partners with Pascal as master distributor for APJ region
10 January 2024, VnExpress International

provided by Google News

OCI Object Storage Completes Technical Validation of Vertica in Eon Mode
16 October 2023, blogs.oracle.com

Vertica by OpenText and Anritsu Sign New Deal for Next-Gen Architecture and 5G Network Capabilities
17 May 2023, PR Newswire

MapR Hadoop Upgrade Runs HP Vertica
22 September 2023, InformationWeek

Stonebraker Seeks to Invert the Computing Paradigm with DBOS
12 March 2024, Datanami

OpenText expands enterprise portfolio with AI and Micro Focus integrations
25 July 2023, VentureBeat

provided by Google News

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