DBMS > Amazon SimpleDB vs. FatDB vs. Spark SQL vs. XTDB

System Properties Comparison Amazon SimpleDB vs. FatDB vs. Spark SQL vs. XTDB

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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

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

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

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Key-value store

Document store
Key-value store

Relational DBMS

Document store

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

Score	1.85
Rank	#138	Overall
	#24	Key-value stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

aws.amazon.com/simpledb

spark.apache.org/sql

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.aws.amazon.com/simpledb

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

www.xtdb.com/docs

Developer

Amazon

FatCloud

Apache Software Foundation

Juxt Ltd.

Initial release

2007

2012

2014

2019

Current release

3.5.0 ( 2.13), September 2023

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

MIT License

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

Scala

Clojure

Server operating systems

hosted

Windows

Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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 columns are indexed automatically

yes

SQL

Support of SQL

Via inetgration in SQL Server

SQL-like DML and DDL statements

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP API

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

JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

Java
Python
R
Scala

Clojure
Java

Server-side scripts

Stored procedures

yes

via applications

Triggers

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Concurrent data updates can be detected by the application

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

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

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More resources

DB-Engines blog posts

The popularity of cloud-based DBMSs has increased tenfold in four years
7 February 2017, Matthias Gelbmann

Amazon - the rising star in the DBMS market
3 August 2015, Matthias Gelbmann

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Recent citations in the news

New SimpleDB Goodies: Enhanced Select, Larger Result Sets, Mandatory HTTPS | Amazon Web Services
20 May 2009, AWS Blog

Hands-on Tutorial for Getting Started with Amazon SimpleDB
28 May 2010, Packt Hub

Amazon DynamoDB Serves Trillions Of Requests Per Month While Counterpart SimpleDB Is No Longer A Listed Product On AWS
12 November 2013, TechCrunch

Amazon SimpleDB Management in Eclipse | AWS News Blog
22 July 2009, AWS Blog

Amazon Goes Back to the Future With 'NoSQL' Database
19 January 2012, WIRED

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

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