DBMS > Google Cloud Bigtable vs. GridDB vs. MonetDB vs. NSDb vs. PieCloudDB

System Properties Comparison Google Cloud Bigtable vs. GridDB vs. MonetDB vs. NSDb vs. PieCloudDB

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.

Scalable in-memory time series database optimized for IoT and Big Data

A relational database management system that stores data in columns

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

A cloud-native analytic database platform with new technologoy for elastic MPP

Primary database model

Key-value store
Wide column store

Secondary database models

Key-value store
Relational DBMS

Document store
Spatial DBMS

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

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.25
Rank	#304	Overall
	#138	Relational DBMS

Website

cloud.google.com/bigtable

Technical documentation

cloud.google.com/bigtable/docs

docs.griddb.net

www.monetdb.org/Documentation

nsdb.io/Architecture

Developer

Google

Toshiba Corporation

MonetDB BV

OpenPie

Initial release

2015

2013

2004

2017

Current release

5.1, August 2022

Dec2023 (11.49), December 2023

2.1, January 2023

License

Commercial or Open Source

commercial

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

Mozilla Public License 2.0

Open Source

Apache Version 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

C++

Java, Scala

Server operating systems

hosted

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS

hosted

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

yes

SQL 2003 with some extensions

SQL-like query language

yes

APIs and other access methods

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

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

gRPC
HTTP REST
WebSocket

CLI Client
JDBC
ODBC

Supported programming languages

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

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

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

Java
Scala

Java
PL/SQL
Python
R

Server-side scripts

Stored procedures

yes, in SQL, C, R

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding via remote tables

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

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

Source-replica replication

none

Source-replica replication available in experimental status

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

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 within container, eventual consistency across containers

Eventual Consistency

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 at container level

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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

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

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

User Roles and pluggable authentication with full SQL Standard

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

PieCloudDB, OpenPie's flagship product, is a cutting-edge cloud-native data warehouse....
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Extreme Elastic: PieCloudDB utilizes a cutting-edge eMPP cloud-native architecture...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

PieCloudDB is ideal for Data mining applications that require extreme scalability...
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Sail-Cloud China Shipbuilding Group Haizhou System Soochow Securities etc.,
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

PieCloudDB Community Edition: Community License, Free Download, Self-Hosted Deployment；...
» more

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

Recent citations in the news

Google's AI-First Strategy Brings Vector Support To Cloud Databases
1 March 2024, Forbes

Google Introduces Autoscaling for Cloud Bigtable for Optimizing Costs
31 January 2022, InfoQ.com

Google scales up Cloud Bigtable NoSQL database
27 January 2022, TechTarget

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

Google Cloud makes it cheaper to run smaller workloads on Bigtable
7 April 2020, TechCrunch

provided by Google News

General Availability of GridDB® 5.5 Enterprise Edition ~Enhancing the efficiency of IoT system development and ...
16 January 2024, global.toshiba

Toshiba launches cloudy managed IoT database service running its own GridDB
8 April 2021, The Register

GridDB Use case Large-scale high-speed processing of smart meter data following the deregulation of electrical power ...
1 November 2020, global.toshiba

General Availability of GridDB 5.1 Enterprise Edition ~ Continuous database usage in the event of data center failure ...
19 August 2022, global.toshiba

IoT: Opt for the Right Open Source Database
11 August 2023, Open Source For You

provided by Google News

In 2024 the MonetDB Foundation was established for the preservation, maintenance and further development of the ...
31 January 2024, Centrum Wiskunde & Informatica (CWI)

MonetDB Secures Investment From (and Partners With) ServiceNow
9 December 2021, Datanami

PostgreSQL, MonetDB, and Too-Big-for-Memory Data in R - Part I - DataScienceCentral.com
6 April 2018, Data Science Central

Test of Time Award for paper on vectorized execution
16 January 2024, Centrum Wiskunde & Informatica (CWI)

How MonetDB Exploits Modern CPU Performance | by Dwi Prasetyo Adi Nugroho
14 January 2020, Towards Data Science

provided by Google News

Share this page