DBMS > Amazon DynamoDB vs. BaseX vs. Spark SQL vs. SWC-DB

System Properties Comparison Amazon DynamoDB vs. BaseX vs. Spark SQL vs. SWC-DB

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Editorial information provided by DB-Engines

Name

Description

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

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

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

A high performance, scalable Wide Column DBMS

Primary database model

Document store
Key-value store

Native XML DBMS

Relational DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	1.84
Rank	#135	Overall
	#4	Native XML DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

aws.amazon.com/dynamodb

basex.org

spark.apache.org/sql

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

docs.aws.amazon.com/dynamodb

docs.basex.org

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

Developer

Amazon

BaseX GmbH

Apache Software Foundation

Alex Kashirin

Initial release

2012

2007

2014

2020

Current release

11.0, June 2024

3.5.0 ( 2.13), September 2023

0.5, April 2021

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

BSD license

Open Source

Apache 2.0

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

C++

Server operating systems

hosted

Linux
OS X
Windows

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

XQuery supports types

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

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

RESTful HTTP API

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

JDBC
ODBC

Proprietary protocol
Thrift

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

Java
Python
R
Scala

C++

Server-side scripts

Stored procedures

yes

Triggers

yes

by integration with AWS Lambda

yes

via events

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

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

multiple readers, single writer

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.

User concepts

Access control

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

Users with fine-grained authorization concept on 4 levels

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