DBMS > BigObject vs. DuckDB vs. OrigoDB vs. Teradata Aster

System Properties Comparison BigObject vs. DuckDB vs. OrigoDB vs. Teradata Aster

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Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

Analytic DBMS for real-time computations and queries

An embeddable, in-process, column-oriented SQL OLAP RDBMS

A fully ACID in-memory object graph database

Platform for big data analytics on multistructured data sources and types

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Document store
Object oriented DBMS

Relational DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	4.57
Rank	#74	Overall
	#40	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Website

bigobject.io

duckdb.org

origodb.com

Technical documentation

docs.bigobject.io

duckdb.org/docs

origodb.com/docs

Developer

BigObject, Inc.

Robert Friberg et al

Teradata

Initial release

2015

2018

2009

under the name LiveDB

2005

Current release

0.10, February 2024

License

Commercial or Open Source

commercial

free community edition available

Open Source

MIT License

Open Source

commercial

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++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

server-less

Linux
Windows

Linux

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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.

can be achieved using .NET

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

.NET Client API
HTTP API
LINQ

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

.Net

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

Lua

yes

R packages

Triggers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

client side managed; servers are not synchronized

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

depending on model

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, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

Write ahead log

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 authorization

fine grained access rights according to SQL-standard

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More resources

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My First Billion (of Rows) in DuckDB | by João Pedro | May, 2024
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DuckDB and AWS — How to Aggregate 100 Million Rows in 1 Minute
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23 May 2014, Northwestern Engineering

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23 April 2015, The Register

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12 September 2016, RCR Wireless News

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