DBMS > Alibaba Cloud MaxCompute vs. Apache IoTDB vs. Hive vs. MonetDB vs. Snowflake

System Properties Comparison Alibaba Cloud MaxCompute vs. Apache IoTDB vs. Hive vs. MonetDB vs. Snowflake

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

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

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

A relational database management system that stores data in columns

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS

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

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.77
Rank	#214	Overall
	#98	Relational DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.alibabacloud.com/product/maxcompute

Technical documentation

www.alibabacloud.com/help/en/maxcompute

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

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

www.monetdb.org/Documentation

docs.snowflake.net/manuals/index.html

Developer

Alibaba

Apache Software Foundation

initially developed by Facebook

MonetDB BV

Snowflake Computing Inc.

Initial release

2016

2018

2012

2004

2014

Current release

1.1.0, April 2023

3.1.3, April 2022

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2

Open Source

Mozilla Public License 2.0

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

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

yes

SQL 2003 with some extensions

yes

APIs and other access methods

Fluentd
Flume
MaxCompute Console

JDBC
Native API

JDBC
ODBC
Thrift

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

Java

C
C#
C++
Go
Java
Python
Scala

C++
Java
PHP
Python

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Java

yes

user defined functions and integration of map-reduce

yes, in SQL, C, R

user defined functions

Triggers

yes

similar concept for controling cloud resources

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)

Sharding

Sharding via remote tables

yes

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

selectable replication factor

none

Source-replica replication available in experimental status

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Integration with Hadoop and Spark

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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 rights for users and roles

yes

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

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