DBMS > Alibaba Cloud MaxCompute vs. Microsoft Azure Data Explorer vs. Newts

System Properties Comparison Alibaba Cloud MaxCompute vs. Microsoft Azure Data Explorer vs. Newts

Please select another system to include it in the comparison.

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

Fully managed big data interactive analytics platform

Time Series DBMS based on Cassandra

Primary database model

Relational DBMS

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

Relational DBMS

column oriented

Time Series DBMS

Secondary database models

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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

Score	0.78
Rank	#221	Overall
	#102	Relational DBMS

Score	5.16
Rank	#69	Overall
	#37	Relational DBMS

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Website

www.alibabacloud.com/product/maxcompute

azure.microsoft.com/services/data-explorer

opennms.github.io/newts

Technical documentation

www.alibabacloud.com/help/en/maxcompute

docs.microsoft.com/en-us/azure/data-explorer

github.com/OpenNMS/newts/wiki

Developer

Alibaba

Microsoft

OpenNMS Group

Initial release

2016

2019

2014

Current release

cloud service with continuous releases

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

Linux
OS X
Windows

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

schema-free

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-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

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

Kusto Query Language (KQL), SQL subset

APIs and other access methods

Fluentd
Flume
MaxCompute Console

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

HTTP REST
Java API

Supported programming languages

Java

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

Java

Server-side scripts

Stored procedures

user defined functions

in Java

Yes, possible languages: KQL, Python, R

Triggers

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

Transaction concepts

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

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.

User concepts

Access control

Access rights for users and roles

Azure Active Directory Authentication

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Alibaba Cloud Brings ‘MaxCompute’ Big Data Service To Europe
5 November 2019, Silicon UK

Alibaba Cloud To Launch Big Data Service in Europe
15 June 2017, Alizila

Alibaba Cloud introduces serverless AI solution to boost enterprise efficiency
31 January 2024, Backend News

Streaming Data Services Comparison: Alibaba Cloud, AWS, Azure, Google Cloud, IBM Cloud
20 January 2020, Wire19

AWS, IBM, Microsoft, Google emerge Cloud DBMS leaders
22 December 2022, Daily Host News

provided by Google News

Azure Data Explorer: Log and telemetry analytics benchmark
16 August 2022, azure.microsoft.com

Providing modern data transfer and storage service at Microsoft with Microsoft Azure - Inside Track Blog
13 July 2023, Microsoft

General availability: New KQL function to enrich your data analysis with geographic context | Azure updates
6 June 2023, azure.microsoft.com

What is Microsoft Fabric? A big tech stack for big data
9 February 2024, InfoWorld

Azure Data Explorer and Stream Analytics for anomaly detection
16 January 2020, azure.microsoft.com

provided by Google News

Share this page