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DBMS > FileMaker vs. Microsoft Azure Data Explorer vs. Oracle Berkeley DB vs. OrigoDB vs. TimescaleDB

System Properties Comparison FileMaker vs. Microsoft Azure Data Explorer vs. Oracle Berkeley DB vs. OrigoDB vs. TimescaleDB

Editorial information provided by DB-Engines
NameFileMaker  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonOrigoDB  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionFileMaker is a cross-platform RDBMS that includes a GUI frontend.Fully managed big data interactive analytics platformWidely used in-process key-value storeA fully ACID in-memory object graph databaseA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMSRelational DBMS infocolumn orientedKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Document store
Object oriented DBMS
Time Series 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
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score48.20
Rank#20  Overall
#14  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score2.21
Rank#117  Overall
#20  Key-value stores
#3  Native XML DBMS
Score0.00
Rank#383  Overall
#53  Document stores
#20  Object oriented DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitewww.claris.com/­filemakerazure.microsoft.com/­services/­data-explorerwww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlorigodb.comwww.timescale.com
Technical documentationwww.claris.com/­resources/­documentationdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.oracle.com/­cd/­E17076_05/­html/­index.htmlorigodb.com/­docsdocs.timescale.com
DeveloperClaris infoa subsidiary of AppleMicrosoftOracle infooriginally developed by Sleepycat, which was acquired by OracleRobert Friberg et alTimescale
Initial release1983201919942009 infounder the name LiveDB2017
Current release19.4.1, November 2021cloud service with continuous releases18.1.40, May 20202.15.0, May 2024
License infoCommercial or Open SourcecommercialcommercialOpen Source infocommercial license availableOpen SourceOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, Java, C++ (depending on the Berkeley DB edition)C#C
Server operating systemsiOS infoclient part only
Linux
OS X
Windows
hostedAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Linux
Windows
Linux
OS X
Windows
Data schemeyesFixed schema with schema-less datatypes (dynamic)schema-freeyesyes
Typing infopredefined data types such as float or dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesnoUser defined using .NET types and collectionsnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex 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.yes infoA http query request can return the data in XML formatyesyes infoonly with the Berkeley DB XML editionno infocan be achieved using .NETyes
Secondary indexesyesall fields are automatically indexedyesyesyes
SQL infoSupport of SQLyes infovia pluginsKusto Query Language (KQL), SQL subsetyes infoSQL interfaced based on SQLite is availablenoyes infofull PostgreSQL SQL syntax
APIs and other access methodsFilemaker WebDirect
JDBC
ODBC
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
.NET Client API
HTTP API
LINQ
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesPHP.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
.Net.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyesYes, possible languages: KQL, Python, Rnoyesuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoonly for the SQL APIyes infoDomain Eventsyes
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infoImplicit feature of the cloud servicenonehorizontal partitioning infoclient side managed; servers are not synchronizedyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replication, warm standby infosince Version 14yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationSource-replica replicationSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparknonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesnonodepending on modelyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoWrite ahead logyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyesno
User concepts infoAccess controlsimple rights management via user accounts and connection to external directory servicesAzure Active Directory AuthenticationnoRole based authorizationfine grained access rights according to SQL-standard

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More resources
FileMakerMicrosoft Azure Data ExplorerOracle Berkeley DBOrigoDBTimescaleDB
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