DBMS > Amazon DynamoDB vs. Apache Phoenix vs. Faircom EDGE vs. Hypertable

System Properties Comparison Amazon DynamoDB vs. Apache Phoenix vs. Faircom EDGE vs. Hypertable

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

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

A scale-out RDBMS with evolutionary schema built on Apache HBase

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

An open source BigTable implementation based on distributed file systems such as Hadoop

Primary database model

Document store
Key-value store

Relational DBMS

Key-value store
Relational DBMS

Wide column store

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Website

aws.amazon.com/dynamodb

phoenix.apache.org

www.faircom.com/products/faircom-edge

Technical documentation

docs.aws.amazon.com/dynamodb

phoenix.apache.org

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

Developer

Amazon

Apache Software Foundation

FairCom Corporation

Hypertable Inc.

Initial release

2012

2014

1979

2009

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

V3, October 2020

0.9.8.11, March 2016

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache Version 2.0

commercial

Restricted, free version available

Open Source

GNU version 3. Commercial license available

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

ANSI C, C++

C++

Server operating systems

hosted

Linux
Unix
Windows

Android
Linux

ARM, x86
Raspbian
Windows

Linux
OS X
Windows

an inofficial Windows port is available

Data scheme

schema-free

yes

late-bound, schema-on-read capabilities

Flexible Schema (defined schema, partial schema, schema free)

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

Secondary indexes

yes

restricted

only exact value or prefix value scans

SQL

Support of SQL

yes

ANSI SQL queries

APIs and other access methods

RESTful HTTP API

JDBC

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

C++ API
Thrift

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

yes

.Net, JavaScript, C/C++

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning

Customizable business rules for partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

selectable replication factor on file system level

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency or Eventual Consistency

Immediate Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

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 can be defined via the AWS Identity and Access Management (IAM)

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

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