DBMS > Databend vs. Google BigQuery vs. MonetDB vs. TimesTen

System Properties Comparison Databend vs. Google BigQuery vs. MonetDB vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Large scale data warehouse service with append-only tables

A relational database management system that stores data in columns

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.28
Rank	#287	Overall
	#131	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	1.72
Rank	#135	Overall
	#62	Relational DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

github.com/datafuselabs/databend
www.databend.com

cloud.google.com/bigquery

www.monetdb.org

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

docs.databend.com

cloud.google.com/bigquery/docs

www.monetdb.org/Documentation

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

Databend Labs

Google

MonetDB BV

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2021

2010

2004

1998

Current release

1.0.59, April 2023

Dec2023 (11.49), December 2023

Release 22.1

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Mozilla Public License 2.0

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

Rust

Server operating systems

hosted
Linux
macOS

hosted

FreeBSD
Linux
OS X
Solaris
Windows

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

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

yes

SQL 2003 with some extensions

yes

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

RESTful HTTP/JSON API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, in SQL, C, R

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding via remote tables

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication available in experimental status

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

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Users with fine-grained authorization concept, user roles

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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