DBMS > Amazon DynamoDB vs. Apache Phoenix vs. Heroic vs. Hive vs. Linter

System Properties Comparison Amazon DynamoDB vs. Apache Phoenix vs. Heroic vs. Hive vs. Linter

Editorial information provided by DB-Engines

Name

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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

RDBMS for high security requirements

Primary database model

Document store
Key-value store

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial 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	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.12
Rank	#350	Overall
	#152	Relational DBMS

Website

aws.amazon.com/dynamodb

phoenix.apache.org

github.com/spotify/heroic

hive.apache.org

linter.ru

Technical documentation

docs.aws.amazon.com/dynamodb

phoenix.apache.org

spotify.github.io/heroic

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

Developer

Amazon

Apache Software Foundation

Spotify

Apache Software Foundation

initially developed by Facebook

relex.ru

Initial release

2012

2014

2012

1990

Current release

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

3.1.3, April 2022

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache Version 2.0

Open Source

Apache 2.0

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 and C++

Server operating systems

hosted

Linux
Unix
Windows

All OS with a Java VM

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Data scheme

schema-free

yes

late-bound, schema-on-read capabilities

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

via Elasticsearch

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP API

JDBC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC
Thrift

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

C++
Java
PHP
Python

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions and integration of map-reduce

yes

proprietary syntax with the possibility to convert from PL/SQL

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Hadoop integration

yes

query execution via MapReduce

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

Eventual Consistency
Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

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

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

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

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