DBMS > Amazon Redshift vs. AnzoGraph DB vs. Apache Phoenix vs. Faircom EDGE vs. Hive

System Properties Comparison Amazon Redshift vs. AnzoGraph DB vs. Apache Phoenix vs. Faircom EDGE vs. Hive

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

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

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

Primary database model

Relational DBMS

Graph DBMS
RDF store

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

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

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Website

aws.amazon.com/redshift

cambridgesemantics.com/anzograph

phoenix.apache.org

www.faircom.com/products/faircom-edge

hive.apache.org

Technical documentation

docs.aws.amazon.com/redshift

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

phoenix.apache.org

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

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

Developer

Amazon (based on PostgreSQL)

Cambridge Semantics

Apache Software Foundation

FairCom Corporation

Apache Software Foundation

initially developed by Facebook

Initial release

2012

2018

2014

1979

2012

Current release

2.3, January 2021

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

V3, October 2020

3.1.3, April 2022

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2.0

commercial

Restricted, free version available

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

ANSI C, C++

Java

Server operating systems

hosted

Linux

Linux
Unix
Windows

Android
Linux

ARM, x86
Raspbian
Windows

All OS with a Java VM

Data scheme

yes

Schema-free and OWL/RDFS-schema support

yes

late-bound, schema-on-read capabilities

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

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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.

yes

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SPARQL and SPARQL* as primary query language. Cypher preview.

yes

ANSI SQL queries

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

JDBC

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

JDBC
ODBC
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
Python

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

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

C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

user defined functions

yes

.Net, JavaScript, C/C++

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

File partitioning

Customizable business rules for partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication in MPP-Cluster

Multi-source replication
Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Hadoop integration

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in MPP-Cluster

Immediate Consistency or Eventual Consistency

Immediate Consistency
Tunable Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

not needed in graphs

yes

when using SQL

Transaction concepts

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

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

fine grained access rights according to SQL-standard

Access rights for users and roles

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.

Access rights for users, groups and roles

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