DBMS > FatDB vs. Google Cloud Firestore vs. NSDb vs. Spark SQL

System Properties Comparison FatDB vs. Google Cloud Firestore vs. NSDb vs. Spark SQL

Please select another system to include it in the comparison.

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 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.

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Document store
Key-value store

Document store

Time Series DBMS

Relational DBMS

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

Score	8.96
Rank	#48	Overall
	#8	Document stores

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

firebase.google.com/products/firestore

nsdb.io

spark.apache.org/sql

Technical documentation

firebase.google.com/docs/firestore

nsdb.io/Architecture

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

FatCloud

Google

Apache Software Foundation

Initial release

2012

2017

2014

Current release

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

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, Scala

Scala

Server operating systems

Windows

hosted

Linux
macOS

Linux
OS X
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

Via inetgration in SQL Server

SQL-like query language

SQL-like DML and DDL statements

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

gRPC
HTTP REST
WebSocket

JDBC
ODBC

Supported programming languages

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

Java
Scala

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

via applications

yes, Firebase Rules & Cloud Functions

Triggers

yes

via applications

yes, with Cloud Functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

none

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

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

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.

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.

More information provided by the system vendor

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

DB-Engines blog posts

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

show all

Recent citations in the news

Realtime vs Cloud Firestore: Which Firebase Database to go?
8 March 2024, Appinventiv

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

Google’s Firebase gets AI extensions, opens up its marketplace
10 May 2023, TechCrunch

Google's Cloud Firestore is now generally available
31 January 2019, ZDNet

Google's Cloud-Native NoSQL Database Cloud Firestore Is Now Generally Available
8 February 2019, InfoQ.com

provided by Google News

Use Amazon Athena with Spark SQL for your open-source transactional table formats | Amazon Web Services
24 January 2024, AWS Blog

What is Apache Spark? The big data platform that crushed Hadoop
3 April 2024, InfoWorld

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024
1 April 2024, Simplilearn

1.5 Years of Spark Knowledge in 8 Tips | by Michael Berk
23 December 2023, Towards Data Science

Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS | Amazon Web Services
18 October 2023, AWS Blog

provided by Google News

Share this page