DBMS > Amazon DocumentDB vs. EventStoreDB vs. Spark SQL vs. Transbase

System Properties Comparison Amazon DocumentDB vs. EventStoreDB vs. Spark SQL vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Industrial-strength, open-source database solution built from the ground up for event sourcing.

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

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Document store

Event Store

Relational DBMS

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

aws.amazon.com/documentdb

www.eventstore.com

spark.apache.org/sql

www.transaction.de/en/products/transbase.html

Technical documentation

aws.amazon.com/documentdb/resources

developers.eventstore.com

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

www.transaction.de/en/products/transbase/features.html

Developer

Event Store Limited

Apache Software Foundation

Transaction Software GmbH

Initial release

2019

2012

2014

1987

Current release

21.2, February 2021

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

free development 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

C and C++

Server operating systems

hosted

Linux
Windows

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

Java
Python
R
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

none

Source-replica replication

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

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

yes

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

fine grained access rights according to SQL-standard

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More resources

Recent citations in the news

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29 November 2023, AWS Blog

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