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DBMS > Alibaba Cloud MaxCompute vs. AnzoGraph DB vs. Google Cloud Datastore vs. Hive vs. Microsoft Azure Data Explorer

System Properties Comparison Alibaba Cloud MaxCompute vs. AnzoGraph DB vs. Google Cloud Datastore vs. Hive vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameAlibaba Cloud MaxCompute  Xexclude from comparisonAnzoGraph DB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHive  Xexclude from comparisonMicrosoft Azure Data Explorer  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 virtualizationAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platformdata warehouse software for querying and managing large distributed datasets, built on HadoopFully managed big data interactive analytics platform
Primary database modelRelational DBMS infoa graph-processing framework is available with the graphs being stored in tablesGraph DBMS
RDF store
Document storeRelational DBMSRelational DBMS infocolumn oriented
Secondary database modelsDocument 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
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
Score4.47
Rank#76  Overall
#12  Document stores
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websitewww.alibabacloud.com/­product/­maxcomputecambridgesemantics.com/­anzographcloud.google.com/­datastorehive.apache.orgazure.microsoft.com/­services/­data-explorer
Technical documentationwww.alibabacloud.com/­help/­en/­maxcomputedocs.cambridgesemantics.com/­anzograph/­userdoc/­home.htmcloud.google.com/­datastore/­docscwiki.apache.org/­confluence/­display/­Hive/­Homedocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperAlibabaCambridge SemanticsGoogleApache Software Foundation infoinitially developed by FacebookMicrosoft
Initial release20162018200820122019
Current release2.3, January 20213.1.3, April 2022cloud service with continuous releases
License infoCommercial or Open Sourcecommercialcommercial infofree trial version availablecommercialOpen Source infoApache Version 2commercial
Cloud-based only infoOnly available as a cloud serviceyesnoyesnoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava
Server operating systemshostedLinuxhostedAll OS with a Java VMhosted
Data schemeyesSchema-free and OWL/RDFS-schema supportschema-freeyesFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyes, details hereyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types
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 indexesnonoyesyesall fields are automatically indexed
SQL infoSupport of SQLSQL-like query languageSPARQL and SPARQL* as primary query language. Cypher preview.SQL-like query language (GQL)SQL-like DML and DDL statementsKusto Query Language (KQL), SQL subset
APIs and other access methodsFluentd
Flume
MaxCompute Console
Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
Thrift
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesJavaC++
Java
Python
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++
Java
PHP
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresuser defined functions infoin Javauser defined functions and aggregatesusing Google App Engineyes infouser defined functions and integration of map-reduceYes, possible languages: KQL, Python, R
TriggersnonoCallbacks using the Google Apps Enginenoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud serviceAutomatic shardingShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoImplicit feature of the cloud serviceMulti-source replication in MPP-ClusterMulti-source replication using Paxosselectable replication factoryes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesKerberos/HDFS data loadingyes infousing Google Cloud Dataflowyes infoquery execution via MapReduceSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency in MPP-ClusterImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Eventual ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynono infonot needed in graphsyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnono
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.noyesnono
User concepts infoAccess controlAccess rights for users and rolesAccess rights for users and rolesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and rolesAzure Active Directory Authentication

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More resources
Alibaba Cloud MaxComputeAnzoGraph DBGoogle Cloud DatastoreHiveMicrosoft Azure Data Explorer
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