DBMS > Alibaba Cloud MaxCompute vs. Amazon Redshift vs. Faircom EDGE vs. Graph Engine vs. HBase

System Properties Comparison Alibaba Cloud MaxCompute vs. Amazon Redshift vs. Faircom EDGE vs. Graph Engine vs. HBase

Editorial information provided by DB-Engines

Name

Description

MaxCompute (previously known as ODPS) is a general purpose, fully managed, multi-tenancy data processing platform for large-scale data warehousing

Large scale data warehouse service for use with business intelligence tools

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Wide-column store based on Apache Hadoop and on concepts of BigTable

Primary database model

Relational DBMS

a graph-processing framework is available with the graphs being stored in tables

Relational DBMS

Key-value store
Relational DBMS

Graph DBMS
Key-value store

Wide column store

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

Score	0.84
Rank	#209	Overall
	#98	Relational DBMS

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Website

www.alibabacloud.com/product/maxcompute

aws.amazon.com/redshift

www.faircom.com/products/faircom-edge

www.graphengine.io

hbase.apache.org

Technical documentation

www.alibabacloud.com/help/en/maxcompute

docs.aws.amazon.com/redshift

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

www.graphengine.io/docs/manual

hbase.apache.org/book.html

Developer

Alibaba

Amazon (based on PostgreSQL)

FairCom Corporation

Microsoft

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Initial release

2016

2012

1979

2010

2008

Current release

V3, October 2020

2.3.4, January 2021

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

MIT License

Open Source

Apache version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

ANSI C, C++

.NET and C

Java

Server operating systems

hosted

Android
Linux

ARM, x86
Raspbian
Windows

.NET

Linux
Unix
Windows

using Cygwin

Data scheme

yes

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

yes

schema-free, schema definition possible

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

options to bring your own types, AVRO

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

SQL-like query language

yes

does not fully support an SQL-standard

yes

ANSI SQL queries

APIs and other access methods

Fluentd
Flume
MaxCompute Console

JDBC
ODBC

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

RESTful HTTP API

Java API
RESTful HTTP API
Thrift

Supported programming languages

Java

All languages supporting JDBC/ODBC

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

C#
C++
F#
Visual Basic

C
C#
C++
Groovy
Java
PHP
Python
Scala

Server-side scripts

Stored procedures

user defined functions

in Java

user defined functions

in Python

yes

.Net, JavaScript, C/C++

yes

Coprocessors in Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

File partitioning

Customizable business rules for partitioning

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

when using SQL

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

fine grained access rights according to SQL-standard

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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