DBMS > Apache Phoenix vs. Faircom EDGE vs. JaguarDB vs. JSqlDb vs. Microsoft Azure Cosmos DB

System Properties Comparison Apache Phoenix vs. Faircom EDGE vs. JaguarDB vs. JSqlDb vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

JSqlDB seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

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

Performant, highly scalable DBMS for AI and IoT applications

JavaScript Query Language Database, stores JavaScript objects and primitives

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Key-value store
Vector DBMS

Document store
Object oriented DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

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

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

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

phoenix.apache.org

www.faircom.com/products/faircom-edge

www.jaguardb.com

jsqldb.org (offline)

azure.microsoft.com/services/cosmos-db

Technical documentation

phoenix.apache.org

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

www.jaguardb.com/support.html

learn.microsoft.com/azure/cosmos-db

Developer

Apache Software Foundation

FairCom Corporation

DataJaguar, Inc.

Konrad von Backstrom

Microsoft

Initial release

2014

1979

2015

2018

2014

Current release

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

V3, October 2020

3.3 July 2023

0.8, December 2018

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Restricted, free version available

Open Source

GPL V3.0

Open Source

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

ANSI C, C++

C++

the server part. Clients available in other languages

Server operating systems

Linux
Unix
Windows

Android
Linux

ARM, x86
Raspbian
Windows

Linux

Linux
macOS
Windows

hosted

Data scheme

yes

late-bound, schema-on-read capabilities

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

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

JSON 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

All properties auto-indexed by default

SQL

Support of SQL

yes

ANSI SQL queries

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like query language

APIs and other access methods

JDBC

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

JDBC
ODBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

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

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

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

JavaScript

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

user defined functions

yes

.Net, JavaScript, C/C++

functions in JavaScript

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning

Customizable business rules for partitioning

Sharding

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication

none

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency
Tunable Consistency

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

when using SQL

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

using RocksDB

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

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

rights management via user accounts

Access rights can be defined down to the item level

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