DBMS > Amazon DynamoDB vs. FatDB vs. Google Cloud Firestore vs. GridGain vs. Spark SQL

System Properties Comparison Amazon DynamoDB vs. FatDB vs. Google Cloud Firestore vs. GridGain vs. Spark SQL

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

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

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.

GridGain is an in-memory computing platform, built on Apache Ignite

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

Primary database model

Document store
Key-value store

Document store

Key-value store
Relational DBMS

Relational DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/dynamodb

firebase.google.com/products/firestore

www.gridgain.com

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/dynamodb

firebase.google.com/docs/firestore

www.gridgain.com/docs/index.html

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

Developer

Amazon

FatCloud

Google

GridGain Systems, Inc.

Apache Software Foundation

Initial release

2012

2017

2007

2014

Current release

GridGain 8.5.1

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

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, C++, .Net

Scala

Server operating systems

hosted

Windows

hosted

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

Via inetgration in SQL Server

ANSI-99 for query and DML statements, subset of DDL

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

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

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

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

C#
C++
Java
PHP
Python
Ruby
Scala

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

via applications

yes, Firebase Rules & Cloud Functions

yes (compute grid and cache interceptors can be used instead)

Triggers

yes

by integration with AWS Lambda

yes

via applications

yes, with Cloud Functions

yes (cache interceptors and events)

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

yes

selectable replication factor

Multi-source replication

yes (replicated cache)

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Using Cloud Dataflow

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

yes

ACID

Concurrency

Support for concurrent manipulation of data

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

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.

Security Hooks for custom implementations

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.