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DBMS > ArcadeDB vs. FileMaker vs. Microsoft Azure Data Explorer vs. OrigoDB vs. Tarantool

System Properties Comparison ArcadeDB vs. FileMaker vs. Microsoft Azure Data Explorer vs. OrigoDB vs. Tarantool

Editorial information provided by DB-Engines
NameArcadeDB  Xexclude from comparisonFileMaker  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonOrigoDB  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionFast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewrittenFileMaker is a cross-platform RDBMS that includes a GUI frontend.Fully managed big data interactive analytics platformA fully ACID in-memory object graph databaseIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelDocument store
Graph DBMS
Key-value store
Time Series DBMS infoin next version
Relational DBMSRelational DBMS infocolumn orientedDocument store
Object oriented DBMS
Document store
Key-value store
Relational DBMS
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
Spatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.02
Rank#366  Overall
#50  Document stores
#38  Graph DBMS
#53  Key-value stores
#36  Time Series DBMS
Score48.20
Rank#20  Overall
#14  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score0.00
Rank#383  Overall
#53  Document stores
#20  Object oriented DBMS
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Websitearcadedb.comwww.claris.com/­filemakerazure.microsoft.com/­services/­data-explorerorigodb.comwww.tarantool.io
Technical documentationdocs.arcadedb.comwww.claris.com/­resources/­documentationdocs.microsoft.com/­en-us/­azure/­data-explorerorigodb.com/­docswww.tarantool.io/­en/­doc
DeveloperArcade DataClaris infoa subsidiary of AppleMicrosoftRobert Friberg et alVK
Initial release2021198320192009 infounder the name LiveDB2008
Current releaseSeptember 202119.4.1, November 2021cloud service with continuous releases2.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen SourceOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC#C and C++
Server operating systemsAll OS with a Java VMiOS infoclient part only
Linux
OS X
Windows
hostedLinux
Windows
BSD
Linux
macOS
Data schemeschema-freeyesFixed schema with schema-less datatypes (dynamic)yesFlexible data schema: relational definition for tables with ability to store json-like documents in columns
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-typesUser defined using .NET types and collectionsstring, double, decimal, uuid, integer, blob, boolean, datetime
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.noyes infoA http query request can return the data in XML formatyesno infocan be achieved using .NETno
Secondary indexesyesyesall fields are automatically indexedyesyes
SQL infoSupport of SQLSQL-like query language, no joinsyes infovia pluginsKusto Query Language (KQL), SQL subsetnoFull-featured ANSI SQL support
APIs and other access methodsJDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin
Filemaker WebDirect
JDBC
ODBC
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
.NET Client API
HTTP API
LINQ
Open binary protocol
Supported programming languagesJavaPHP.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.NetC
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresyesYes, possible languages: KQL, Python, RyesLua, C and SQL stored procedures
Triggersyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoDomain Eventsyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infoImplicit feature of the cloud servicehorizontal partitioning infoclient side managed; servers are not synchronizedSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationSource-replica replication, warm standby infosince Version 14yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Casual consistency across sharding partitions
Eventual consistency within replicaset partition infowhen using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition infowhen using Raft
Foreign keys infoReferential integrityyes inforelationship in graphsyesnodepending on modelyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonoACIDACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, cooperative multitasking
Durability infoSupport for making data persistentyesyesyesyes infoWrite ahead logyes, write ahead logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlsimple rights management via user accounts and connection to external directory servicesAzure Active Directory AuthenticationRole based authorizationAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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More resources
ArcadeDBFileMakerMicrosoft Azure Data ExplorerOrigoDBTarantool
DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021,  Vladimir Perepelytsya, Tarantool (sponsor) 

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