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DBMS > Apache Druid vs. Firebase Realtime Database vs. Microsoft Azure Data Explorer vs. OrigoDB vs. TimescaleDB

System Properties Comparison Apache Druid vs. Firebase Realtime Database vs. Microsoft Azure Data Explorer vs. OrigoDB vs. TimescaleDB

Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonOrigoDB  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.Fully managed big data interactive analytics platformA fully ACID in-memory object graph databaseA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMS
Time Series DBMS
Document storeRelational DBMS infocolumn orientedDocument 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
Score3.34
Rank#88  Overall
#48  Relational DBMS
#7  Time Series DBMS
Score14.29
Rank#39  Overall
#6  Document stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score0.00
Rank#383  Overall
#53  Document stores
#20  Object oriented DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitedruid.apache.orgfirebase.google.com/­products/­realtime-databaseazure.microsoft.com/­services/­data-explorerorigodb.comwww.timescale.com
Technical documentationdruid.apache.org/­docs/­latest/­designfirebase.google.com/­docs/­databasedocs.microsoft.com/­en-us/­azure/­data-explorerorigodb.com/­docsdocs.timescale.com
DeveloperApache Software Foundation and contributorsGoogle infoacquired by Google 2014MicrosoftRobert Friberg et alTimescale
Initial release2012201220192009 infounder the name LiveDB2017
Current release29.0.1, April 2024cloud service with continuous releases2.13.0, November 2023
License infoCommercial or Open SourceOpen Source infoApache license v2commercialcommercialOpen SourceOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC#C
Server operating systemsLinux
OS X
Unix
hostedhostedLinux
Windows
Linux
OS X
Windows
Data schemeyes infoschema-less columns are supportedschema-freeFixed schema with schema-less datatypes (dynamic)yesyes
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 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.nonoyesno infocan be achieved using .NETyes
Secondary indexesyesyesall fields are automatically indexedyesyes
SQL infoSupport of SQLSQL for queryingnoKusto Query Language (KQL), SQL subsetnoyes infofull PostgreSQL SQL syntax
APIs and other access methodsJDBC
RESTful HTTP/JSON API
Android
iOS
JavaScript API
RESTful HTTP API
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 languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
Java
JavaScript
Objective-C
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresnolimited functionality with using 'rules'Yes, possible languages: KQL, Python, Ryesuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
TriggersnoCallbacks are triggered when data changesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoDomain Eventsyes
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedSharding infoImplicit feature of the cloud servicehorizontal partitioning infoclient side managed; servers are not synchronizedyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Eventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynononodepending on modelyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoyesnoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
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.nonoyesno
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemyes, based on authentication and database rulesAzure Active Directory AuthenticationRole based authorizationfine grained access rights according to SQL-standard

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More resources
Apache DruidFirebase Realtime DatabaseMicrosoft Azure Data ExplorerOrigoDBTimescaleDB
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