DBMS > atoti vs. FatDB vs. Google Cloud Firestore vs. Hawkular Metrics

System Properties Comparison atoti vs. FatDB vs. Google Cloud Firestore vs. Hawkular Metrics

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Primary database model

Object oriented DBMS

Document store
Key-value store

Document store

Time Series DBMS

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

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Website

atoti.io

firebase.google.com/products/firestore

www.hawkular.org

Technical documentation

docs.atoti.io

firebase.google.com/docs/firestore

www.hawkular.org/hawkular-metrics/docs/user-guide

Developer

ActiveViam

FatCloud

Google

Community supported by Red Hat

Initial release

2012

2017

2014

License

Commercial or Open Source

commercial

free versions available

commercial

Open Source

Apache 2.0

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

Java

Server operating systems

Windows

hosted

Linux
OS X
Windows

Data scheme

schema-free

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

Multidimensional Expressions (MDX)

Via inetgration in SQL Server

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

HTTP REST

Supported programming languages

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

Go
Java
Python
Ruby

Server-side scripts

Stored procedures

Python

yes

via applications

yes, Firebase Rules & Cloud Functions

Triggers

yes

via applications

yes, with Cloud Functions

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

Transaction concepts

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

yes

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

Can implement custom security layer via applications

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

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