DBMS > Alibaba Cloud Table Store vs. Apache IoTDB vs. Faircom EDGE vs. Postgres-XL vs. SAP IQ

System Properties Comparison Alibaba Cloud Table Store vs. Apache IoTDB vs. Faircom EDGE vs. Postgres-XL vs. SAP IQ

Editorial information provided by DB-Engines

Name

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Columnar RDBMS optimized for Big Data analytics

Primary database model

Wide column store

Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

column-oriented

Secondary database models

Document store
Spatial DBMS

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

Score	0.31
Rank	#297	Overall
	#11	Wide column stores

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

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

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	2.64
Rank	#108	Overall
	#52	Relational DBMS

Website

www.alibabacloud.com/product/table-store

iotdb.apache.org

www.faircom.com/products/faircom-edge

www.postgres-xl.org

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

Technical documentation

www.alibabacloud.com/help/en/tablestore

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

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

www.postgres-xl.org/documentation

help.sap.com/docs/SAP_IQ

Developer

Alibaba

Apache Software Foundation

FairCom Corporation

SAP, formerly Sybase

Initial release

2016

2018

1979

2014

since 2012, originally named StormDB

1994

Current release

1.1.0, April 2023

V3, October 2020

10 R1, October 2018

16.1 SPS04, April 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Restricted, free version available

Open Source

Mozilla public license

commercial

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++

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

Android
Linux

ARM, x86
Raspbian
Windows

Linux
macOS

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

yes

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

ANSI SQL queries

yes

distributed, parallel query execution

yes

APIs and other access methods

HTTP API

JDBC
Native API

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

ADO.NET
JDBC
ODBC
RESTful HTTP API

Supported programming languages

Java
Python

C
C#
C++
Go
Java
Python
Scala

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
C#
C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

File partitioning

Customizable business rules for partitioning

horizontal partitioning

shared disk or shared nothing architectures with SAP IQ Multiplexer

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

Synchronous and asynchronous realtime replication based on transaction logs

SAP/Sybase Replication Server

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

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

Atomic single-row operations

ACID

MVCC

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

Access rights based on subaccounts and tokens

yes

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

fine grained access rights according to SQL-standard

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