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DBMS > Alibaba Cloud MaxCompute vs. AnzoGraph DB vs. Kinetica vs. Microsoft Azure Data Explorer vs. RavenDB

System Properties Comparison Alibaba Cloud MaxCompute vs. AnzoGraph DB vs. Kinetica vs. Microsoft Azure Data Explorer vs. RavenDB

Editorial information provided by DB-Engines
NameAlibaba Cloud MaxCompute  Xexclude from comparisonAnzoGraph DB  Xexclude from comparisonKinetica  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonRavenDB  Xexclude from comparison
DescriptionMaxCompute (previously known as ODPS) is a general purpose, fully managed, multi-tenancy data processing platform for large-scale data warehousingScalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualizationFully vectorized database across both GPUs and CPUsFully managed big data interactive analytics platformOpen Source Operational and Transactional Enterprise NoSQL Document Database
Primary database modelRelational DBMS infoa graph-processing framework is available with the graphs being stored in tablesGraph DBMS
RDF store
Relational DBMSRelational DBMS infocolumn orientedDocument store
Secondary database modelsSpatial DBMS
Time Series DBMS
Document store infoIf 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 infothis 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 infosupport 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 infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
Graph DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.77
Rank#214  Overall
#98  Relational DBMS
Score0.23
Rank#307  Overall
#24  Graph DBMS
#13  RDF stores
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score2.92
Rank#101  Overall
#18  Document stores
Websitewww.alibabacloud.com/­product/­maxcomputecambridgesemantics.com/­anzographwww.kinetica.comazure.microsoft.com/­services/­data-explorerravendb.net
Technical documentationwww.alibabacloud.com/­help/­en/­maxcomputedocs.cambridgesemantics.com/­anzograph/­userdoc/­home.htmdocs.kinetica.comdocs.microsoft.com/­en-us/­azure/­data-explorerravendb.net/­docs
DeveloperAlibabaCambridge SemanticsKineticaMicrosoftHibernating Rhinos
Initial release20162018201220192010
Current release2.3, January 20217.1, August 2021cloud service with continuous releases5.4, July 2022
License infoCommercial or Open Sourcecommercialcommercial infofree trial version availablecommercialcommercialOpen Source infoAGPL version 3, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++C#
Server operating systemshostedLinuxLinuxhostedLinux
macOS
Raspberry Pi
Windows
Data schemeyesSchema-free and OWL/RDFS-schema supportyesFixed schema with schema-less datatypes (dynamic)schema-free
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesno
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nononoyes
Secondary indexesnonoyesall fields are automatically indexedyes
SQL infoSupport of SQLSQL-like query languageSPARQL and SPARQL* as primary query language. Cypher preview.SQL-like DML and DDL statementsKusto Query Language (KQL), SQL subsetSQL-like query language (RQL)
APIs and other access methodsFluentd
Flume
MaxCompute Console
Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL
JDBC
ODBC
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API
Supported programming languagesJavaC++
Java
Python
C++
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions infoin Javauser defined functions and aggregatesuser defined functionsYes, possible languages: KQL, Python, Ryes
Triggersnonoyes infotriggers when inserted values for one or more columns fall within a specified rangeyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud serviceAutomatic shardingShardingSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoImplicit feature of the cloud serviceMulti-source replication in MPP-ClusterSource-replica replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Multi-source replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesKerberos/HDFS data loadingnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency in MPP-ClusterImmediate Consistency or Eventual Consistency depending on configurationEventual Consistency
Immediate Consistency
Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.
Foreign keys infoReferential integritynono infonot needed in graphsyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnonoACID, Cluster-wide transaction available
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes infoGPU vRAM or System RAMno
User concepts infoAccess controlAccess rights for users and rolesAccess rights for users and rolesAccess rights for users and roles on table levelAzure Active Directory AuthenticationAuthorization levels configured per client per database

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More resources
Alibaba Cloud MaxComputeAnzoGraph DBKineticaMicrosoft Azure Data ExplorerRavenDB
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