DBMS > BigObject vs. Drizzle vs. HEAVY.AI vs. Trafodion

System Properties Comparison BigObject vs. Drizzle vs. HEAVY.AI vs. Trafodion

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Analytic DBMS for real-time computations and queries

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

Transactional SQL-on-Hadoop DBMS

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Website

bigobject.io

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

trafodion.apache.org

Technical documentation

docs.bigobject.io

docs.heavy.ai

trafodion.apache.org/documentation.html

Developer

BigObject, Inc.

Drizzle project, originally started by Brian Aker

HEAVY.AI, Inc.

Apache Software Foundation, originally developed by HP

Initial release

2015

2008

2016

2014

Current release

7.2.4, September 2012

5.10, January 2022

2.3.0, February 2019

License

Commercial or Open Source

commercial

free community edition available

Open Source

GNU GPL

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

C++ and CUDA

C++, Java

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
OS X

Linux

Data scheme

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

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JDBC

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
ODBC

Supported programming languages

C
C++
Java
PHP

All languages supporting JDBC/ODBC/Thrift
Python

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

Lua

Java Stored Procedures

Triggers

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

Multi-source replication

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
1 April 2015, Paul Andlinger

Has MySQL finally lost its mojo?
1 July 2013, Matthias Gelbmann

show all

Recent citations in the news

Big Data Analytics: A Game Changer for Infrastructure
13 July 2023, Spiceworks News and Insights

HEAVY.AI Launches HEAVY 7.0, Introducing Real-Time Machine Learning Capabilities
19 April 2023, Business Wire

Making the most of geospatial intelligence
14 April 2023, InfoWorld

OmniSci Gets HEAVY New Name and New CEO
1 March 2022, Datanami

The insideBIGDATA IMPACT 50 List for Q4 2023
11 October 2023, insideBIGDATA

provided by Google News

Evaluating HTAP Databases for Machine Learning Applications
2 November 2016, KDnuggets

Low-latency, distributed database architectures are critical for emerging fog applications
7 April 2022, Embedded Computing Design

provided by Google News

Share this page