DBMS > FatDB vs. Firebase Realtime Database vs. Google Cloud Datastore vs. Graphite vs. SAP HANA

System Properties Comparison FatDB vs. Firebase Realtime Database vs. Google Cloud Datastore vs. Graphite vs. SAP HANA

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

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

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Document store
Key-value store

Document store

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	4.57
Rank	#73	Overall
	#5	Time Series DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

firebase.google.com/products/realtime-database

cloud.google.com/datastore

github.com/graphite-project/graphite-web

www.sap.com/products/hana.html

Technical documentation

firebase.google.com/docs/database

cloud.google.com/datastore/docs

graphite.readthedocs.io

help.sap.com/hana

Developer

FatCloud

Google

acquired by Google 2014

Google

Chris Davis

SAP

Initial release

2012

2008

2006

2010

Current release

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Python

Server operating systems

Windows

hosted

Linux
Unix

Appliance or cloud-service

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

Numeric data only

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

Via inetgration in SQL Server

SQL-like query language (GQL)

yes

APIs and other access methods

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

Android
iOS
JavaScript API
RESTful HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

HTTP API
Sockets

JDBC
ODBC

Supported programming languages

Java
JavaScript
Objective-C

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

via applications

limited functionality with using 'rules'

using Google App Engine

SQLScript, R

Triggers

yes

via applications

Callbacks are triggered when data changes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication using Paxos

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

none

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

yes

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

locking

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

yes, based on authentication and database rules

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

yes

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