DBMS > Apache Phoenix vs. JaguarDB vs. Microsoft Azure Table Storage vs. Stardog

System Properties Comparison Apache Phoenix vs. JaguarDB vs. Microsoft Azure Table Storage vs. Stardog

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Editorial information provided by DB-Engines

Name

Description

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

Performant, highly scalable DBMS for AI and IoT applications

A Wide Column Store for rapid development using massive semi-structured datasets

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Relational DBMS

Key-value store
Vector DBMS

Wide column store

Graph DBMS
RDF store

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

phoenix.apache.org

www.jaguardb.com

azure.microsoft.com/en-us/services/storage/tables

www.stardog.com

Technical documentation

phoenix.apache.org

www.jaguardb.com/support.html

docs.stardog.com

Developer

Apache Software Foundation

DataJaguar, Inc.

Microsoft

Stardog-Union

Initial release

2014

2015

2012

2010

Current release

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

3.3 July 2023

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPL V3.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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++

the server part. Clients available in other languages

Java

Server operating systems

Linux
Unix
Windows

Linux

hosted

Linux
macOS
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

yes

schema-free

schema-free and OWL/RDFS-schema support

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

JDBC

JDBC
ODBC

RESTful HTTP API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

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

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Eventual Consistency

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

optimistic locking

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

rights management via user accounts

Access rights based on private key authentication or shared access signatures

Access rights for users and roles

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More resources

DB-Engines blog posts

Cloudera's HBase PaaS offering now supports Complex Transactions
11 August 2021, Krishna Maheshwari (sponsor)

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