DBMS > Graph Engine vs. HEAVY.AI vs. TempoIQ vs. Yaacomo

System Properties Comparison Graph Engine vs. HEAVY.AI vs. TempoIQ vs. Yaacomo

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Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

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

Description

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

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

Primary database model

Graph DBMS
Key-value store

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.62
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Website

www.graphengine.io

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

tempoiq.com (offline)

yaacomo.com

Technical documentation

www.graphengine.io/docs/manual

docs.heavy.ai

Developer

Microsoft

HEAVY.AI, Inc.

TempoIQ

Q2WEB GmbH

Initial release

2010

2016

2012

2009

Current release

5.10, January 2022

License

Commercial or Open Source

Open Source

MIT License

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

.NET and C

C++ and CUDA

Server operating systems

.NET

Linux

Android
Linux
Windows

Data scheme

yes

schema-free

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

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
Thrift
Vega

HTTP API

JDBC
ODBC

Supported programming languages

C#
C++
F#
Visual Basic

All languages supporting JDBC/ODBC/Thrift
Python

C#
Java
JavaScript

Node.js
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes

Realtime Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Round robin

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

simple authentication-based access control

fine grained access rights according to SQL-standard

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