DBMS > Amazon DocumentDB vs. FatDB vs. Microsoft Azure Cosmos DB vs. Trino vs. Yaacomo

System Properties Comparison Amazon DocumentDB vs. FatDB vs. Microsoft Azure Cosmos DB vs. Trino vs. Yaacomo

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.

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

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

Globally distributed, horizontally scalable, multi-model database service

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Document store

Document store
Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

Score	1.91
Rank	#131	Overall
	#24	Document stores

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	4.99
Rank	#65	Overall
	#36	Relational DBMS

Website

aws.amazon.com/documentdb

azure.microsoft.com/services/cosmos-db

trino.io

yaacomo.com

Technical documentation

aws.amazon.com/documentdb/resources

learn.microsoft.com/azure/cosmos-db

trino.io/broadcast
trino.io/docs/current

Social network pages

Developer

FatCloud

Microsoft

Trino Software Foundation

Q2WEB GmbH

Initial release

2019

2012

2014

2012

2020 rebranded from PrestoSQL

2009

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

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.

Starburst Galaxy offers a feature-rich user interface to connect all your data sources, manage your Trino clusters, and query your data.

Implementation language

Java

Server operating systems

hosted

Windows

hosted

Linux
macOS

for devlopment

Android
Linux
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON 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

All properties auto-indexed by default

depending on connected data-source

yes

SQL

Support of SQL

Via inetgration in SQL Server

SQL-like query language

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

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

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
RESTful HTTP API
Trino CLI

JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

yes

via applications

JavaScript

yes, depending on connected data-source

Triggers

yes

via applications

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

depending on connected data-source

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

selectable replication factor

yes

Implicit feature of the cloud service

depending on connected data-source

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

depending on connected data-source

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

Multi-item ACID transactions with snapshot isolation within a partition

depending on connected data-source

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

depending on connected data-source

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 implement custom security layer via applications

Access rights can be defined down to the item level

SQL standard access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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