DBMS > Google Cloud Bigtable vs. MonetDB vs. Sadas Engine vs. SiriDB vs. SQLite

System Properties Comparison Google Cloud Bigtable vs. MonetDB vs. Sadas Engine vs. SiriDB vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A relational database management system that stores data in columns

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Open Source Time Series DBMS

Widely used embeddable, in-process RDBMS

Primary database model

Key-value store
Wide column store

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

cloud.google.com/bigtable

Technical documentation

cloud.google.com/bigtable/docs

www.monetdb.org/Documentation

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

docs.siridb.com

www.sqlite.org/docs.html

Developer

Google

MonetDB BV

SADAS s.r.l.

Cesbit

Dwayne Richard Hipp

Initial release

2015

2004

2006

2017

2000

Current release

Dec2023 (11.49), December 2023

8.0

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

commercial

free trial version available

Open Source

MIT License

Open Source

Public Domain

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

C++

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

AIX
Linux
Windows

Linux

server-less

Data scheme

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

Numeric data

yes

not rigid because of 'dynamic typing' concept.

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

SQL-92 is not fully supported

APIs and other access methods

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

JDBC
ODBC
Proprietary protocol

HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

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

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

.Net
C
C#
C++
Groovy
Java
PHP
Python

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding via remote tables

horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

none

Source-replica replication available in experimental status

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

managed by 'Learn by Usage'

yes

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

simple rights management via user accounts

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