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DBMS > AnzoGraph DB vs. Fauna vs. Google Cloud Datastore vs. JanusGraph vs. Microsoft Azure Data Explorer

System Properties Comparison AnzoGraph DB vs. Fauna vs. Google Cloud Datastore vs. JanusGraph vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameAnzoGraph DB  Xexclude from comparisonFauna infopreviously named FaunaDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonJanusGraph infosuccessor of Titan  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionScalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualizationFauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA Graph DBMS optimized for distributed clusters infoIt was forked from the latest code base of Titan in January 2017Fully managed big data interactive analytics platform
Primary database modelGraph DBMS
RDF store
Document store
Graph DBMS
Relational DBMS
Time Series DBMS
Document storeGraph 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
Score0.23
Rank#307  Overall
#24  Graph DBMS
#13  RDF stores
Score1.52
Rank#153  Overall
#26  Document stores
#14  Graph DBMS
#71  Relational DBMS
#13  Time Series DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score1.94
Rank#129  Overall
#12  Graph DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websitecambridgesemantics.com/­anzographfauna.comcloud.google.com/­datastorejanusgraph.orgazure.microsoft.com/­services/­data-explorer
Technical documentationdocs.cambridgesemantics.com/­anzograph/­userdoc/­home.htmdocs.fauna.comcloud.google.com/­datastore/­docsdocs.janusgraph.orgdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperCambridge SemanticsFauna, Inc.GoogleLinux Foundation; originally developed as Titan by AureliusMicrosoft
Initial release20182014200820172019
Current release2.3, January 20210.6.3, February 2023cloud service with continuous releases
License infoCommercial or Open Sourcecommercial infofree trial version availablecommercialcommercialOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenoyesyesnoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageScalaJava
Server operating systemsLinuxhostedhostedLinux
OS X
Unix
Windows
hosted
Data schemeSchema-free and OWL/RDFS-schema supportschema-freeschema-freeyesFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or datenoyes, 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.nonononoyes
Secondary indexesnoyesyesyesall fields are automatically indexed
SQL infoSupport of SQLSPARQL and SPARQL* as primary query language. Cypher preview.noSQL-like query language (GQL)noKusto Query Language (KQL), SQL subset
APIs and other access methodsApache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL
RESTful HTTP APIgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesC++
Java
Python
C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Clojure
Java
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresuser defined functions and aggregatesuser defined functionsusing Google App EngineyesYes, possible languages: KQL, Python, R
TriggersnonoCallbacks using the Google Apps Engineyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesAutomatic shardinghorizontal partitioning infoconsistent hashingShardingyes infodepending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)Sharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication in MPP-ClusterMulti-source replicationMulti-source replication using Paxosyesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsKerberos/HDFS data loadingnoyes infousing Google Cloud Dataflowyes infovia Faunus, a graph analytics engineSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency in MPP-ClusterImmediate ConsistencyImmediate 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 Consistency
Immediate Consistency
Eventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityno infonot needed in graphsyesyes infovia ReferenceProperties or Ancestor pathsyes infoRelationships in graphsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoSupports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcastyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnonono
User concepts infoAccess controlAccess rights for users and rolesIdentity management, authentication, and access controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)User authentification and security via Rexster Graph ServerAzure Active Directory Authentication

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More resources
AnzoGraph DBFauna infopreviously named FaunaDBGoogle Cloud DatastoreJanusGraph infosuccessor of TitanMicrosoft Azure Data Explorer
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