DBMS > Alibaba Cloud MaxCompute vs. Heroic vs. InterSystems IRIS vs. Microsoft SQL Server vs. Splice Machine

System Properties Comparison Alibaba Cloud MaxCompute vs. Heroic vs. InterSystems IRIS vs. Microsoft SQL Server vs. Splice Machine

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A containerised multi-model DBMS, interoperability and analytics data platform with wide capabilities for vertical and horizontal scalability

Microsofts flagship relational DBMS

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

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

Time Series DBMS

Document store
Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.77
Rank	#214	Overall
	#98	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	4.05
Rank	#83	Overall
	#14	Document stores
	#10	Key-value stores
	#1	Object oriented DBMS
	#45	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

www.alibabacloud.com/product/maxcompute

github.com/spotify/heroic

www.intersystems.com/products/intersystems-iris

www.microsoft.com/en-us/sql-server

splicemachine.com

Technical documentation

www.alibabacloud.com/help/en/maxcompute

spotify.github.io/heroic

docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls

learn.microsoft.com/en-US/sql/sql-server

splicemachine.com/how-it-works

Developer

Alibaba

Spotify

InterSystems

Microsoft

Splice Machine

Initial release

2016

2014

2018

1989

2014

Current release

2023.3, June 2023

SQL Server 2022, November 2022

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

restricted free version is available

Open Source

AGPL 3.0, commercial license available

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

C++

Java

Server operating systems

hosted

AIX
Linux
macOS
Ubuntu
Windows

Linux
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

depending on used data model

yes

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

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

Fluentd
Flume
MaxCompute Console

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Java

.Net
C++
Java
JavaScript (Node.js)
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Java

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

Source-replica replication

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate 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

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

Specific characteristics

InterSystems IRIS is a complete cloud-first data platform which includes a multi-model...
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