DBMS > Amazon DocumentDB vs. Amazon DynamoDB vs. FileMaker vs. Hive vs. Transwarp Hippo

System Properties Comparison Amazon DocumentDB vs. Amazon DynamoDB vs. FileMaker vs. Hive vs. Transwarp Hippo

Editorial information provided by DB-Engines

Name

Description

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

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

FileMaker is a cross-platform RDBMS that includes a GUI frontend.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Document store

Document store
Key-value store

Relational DBMS

Vector DBMS

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

Score	1.91
Rank	#131	Overall
	#24	Document stores

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

Score	47.91
Rank	#20	Overall
	#14	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.05
Rank	#386	Overall
	#15	Vector DBMS

Website

aws.amazon.com/documentdb

aws.amazon.com/dynamodb

www.claris.com/filemaker

hive.apache.org

www.transwarp.cn/en/subproduct/hippo

Technical documentation

aws.amazon.com/documentdb/resources

docs.aws.amazon.com/dynamodb

www.claris.com/resources/documentation

cwiki.apache.org/confluence/display/Hive/Home

Developer

Amazon

Claris

a subsidiary of Apple

Apache Software Foundation

initially developed by Facebook

Initial release

2019

2012

1983

2012

2023

Current release

19.4.1, November 2021

3.1.3, April 2022

1.0, May 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

Apache Version 2

commercial

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

C++

Server operating systems

hosted

iOS

client part only
Linux
OS X
Windows

All OS with a Java VM

Linux
macOS

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

A http query request can return the data in XML format

Secondary indexes

yes

SQL

Support of SQL

yes

via plugins

SQL-like DML and DDL statements

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

RESTful HTTP API

Filemaker WebDirect
JDBC
ODBC

JDBC
ODBC
Thrift

RESTful HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

PHP

C++
Java
PHP
Python

C++
Java
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

Source-replica replication, warm standby

since Version 14

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

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

ACID

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

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

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

simple rights management via user accounts and connection to external directory services

Access rights for users, groups and roles

Role based access control and fine grained access rights

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