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DBMS > Apache Druid vs. Firebase Realtime Database vs. Microsoft Azure Data Explorer vs. Splice Machine vs. SwayDB

System Properties Comparison Apache Druid vs. Firebase Realtime Database vs. Microsoft Azure Data Explorer vs. Splice Machine vs. SwayDB

Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSplice Machine  Xexclude from comparisonSwayDB  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 platformOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and SparkAn embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage
Primary database modelRelational DBMS
Time Series DBMS
Document storeRelational DBMS infocolumn orientedRelational DBMSKey-value store
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
Score3.25
Rank#90  Overall
#47  Relational DBMS
#7  Time Series DBMS
Score13.64
Rank#39  Overall
#6  Document stores
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Score0.04
Rank#387  Overall
#61  Key-value stores
Websitedruid.apache.orgfirebase.google.com/­products/­realtime-databaseazure.microsoft.com/­services/­data-explorersplicemachine.comswaydb.simer.au
Technical documentationdruid.apache.org/­docs/­latest/­designfirebase.google.com/­docs/­databasedocs.microsoft.com/­en-us/­azure/­data-explorersplicemachine.com/­how-it-works
DeveloperApache Software Foundation and contributorsGoogle infoacquired by Google 2014MicrosoftSplice MachineSimer Plaha
Initial release20122012201920142018
Current release29.0.1, April 2024cloud service with continuous releases3.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache license v2commercialcommercialOpen Source infoAGPL 3.0, commercial license availableOpen Source infoGNU Affero GPL V3.0
Cloud-based only infoOnly available as a cloud servicenoyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaScala
Server operating systemsLinux
OS X
Unix
hostedhostedLinux
OS X
Solaris
Windows
Data schemeyes infoschema-less columns are supportedschema-freeFixed schema with schema-less datatypes (dynamic)yesschema-free
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-typesyesno
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
Secondary indexesyesyesall fields are automatically indexedyesno
SQL infoSupport of SQLSQL for queryingnoKusto Query Language (KQL), SQL subsetyesno
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
JDBC
Native Spark Datasource
ODBC
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
Java
JavaScript
Objective-C
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Java
Kotlin
Scala
Server-side scripts infoStored proceduresnolimited functionality with using 'rules'Yes, possible languages: KQL, Python, Ryes infoJavano
TriggersnoCallbacks are triggered when data changesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyesno
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedSharding infoImplicit feature of the cloud serviceShared Nothhing Auto-Sharding, Columnar Partitioningnone
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.Multi-source replication
Source-replica replication
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkYes, via Full Spark Integrationno
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 ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoyesnoACIDAtomic execution of operations
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)yes
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.nonoyesyes
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 AuthenticationAccess rights for users, groups and roles according to SQL-standardno

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More resources
Apache DruidFirebase Realtime DatabaseMicrosoft Azure Data ExplorerSplice MachineSwayDB
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