DBMS > Amazon DynamoDB vs. Apache Phoenix vs. Databricks vs. FatDB vs. STSdb

System Properties Comparison Amazon DynamoDB vs. Apache Phoenix vs. Databricks vs. FatDB vs. STSdb

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.

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

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Document store
Key-value store

Relational DBMS

Document store
Relational DBMS

Document store
Key-value store

Key-value 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	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

aws.amazon.com/dynamodb

phoenix.apache.org

www.databricks.com

github.com/STSSoft/STSdb4

Technical documentation

docs.aws.amazon.com/dynamodb

phoenix.apache.org

docs.databricks.com

Developer

Amazon

Apache Software Foundation

Databricks

FatCloud

STS Soft SC

Initial release

2012

2014

2013

2012

2011

Current release

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

4.0.8, September 2015

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache Version 2.0

commercial

Open Source

GPLv2, 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

Server operating systems

hosted

Linux
Unix
Windows

hosted

Windows

Data scheme

schema-free

yes

late-bound, schema-on-read capabilities

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

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

SQL

Support of SQL

yes

with Databricks SQL

Via inetgration in SQL Server

APIs and other access methods

RESTful HTTP API

JDBC

JDBC
ODBC
RESTful HTTP API

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

.NET Client API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

user defined functions

user defined functions and aggregates

yes

via applications

Triggers

yes

by integration with AWS Lambda

yes

via applications

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

none

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

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

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

Can implement custom security layer via applications

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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