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DBMS > Apache Druid vs. Faircom DB vs. Firebase Realtime Database vs. Microsoft Azure Data Explorer vs. SwayDB

System Properties Comparison Apache Druid vs. Faircom DB vs. Firebase Realtime Database vs. Microsoft Azure Data Explorer vs. SwayDB

Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSwayDB  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataNative high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.Cloud-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 platformAn embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage
Primary database modelRelational DBMS
Time Series DBMS
Key-value store
Relational DBMS
Document storeRelational DBMS infocolumn orientedKey-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
Score0.29
Rank#304  Overall
#43  Key-value stores
#136  Relational DBMS
Score13.64
Rank#39  Overall
#6  Document stores
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.04
Rank#387  Overall
#61  Key-value stores
Websitedruid.apache.orgwww.faircom.com/­products/­faircom-dbfirebase.google.com/­products/­realtime-databaseazure.microsoft.com/­services/­data-explorerswaydb.simer.au
Technical documentationdruid.apache.org/­docs/­latest/­designdocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmlfirebase.google.com/­docs/­databasedocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software Foundation and contributorsFairCom CorporationGoogle infoacquired by Google 2014MicrosoftSimer Plaha
Initial release20121979201220192018
Current release29.0.1, April 2024V12, November 2020cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache license v2commercial infoRestricted, free version availablecommercialcommercialOpen Source infoGNU Affero GPL V3.0
Cloud-based only infoOnly available as a cloud servicenonoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaANSI C, C++Scala
Server operating systemsLinux
OS X
Unix
AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
hostedhosted
Data schemeyes infoschema-less columns are supportedschema free, schema optional, schema required, partial schema,schema-freeFixed schema with schema-less datatypes (dynamic)schema-free
Typing infopredefined data types such as float or dateyesyes, ANSI SQL Types, JSON, typed binary structuresyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesno
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.nononoyesno
Secondary indexesyesyesyesall fields are automatically indexedno
SQL infoSupport of SQLSQL for queryingyes, ANSI SQL with proprietary extensionsnoKusto Query Language (KQL), SQL subsetno
APIs and other access methodsJDBC
RESTful HTTP/JSON API
ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
Android
iOS
JavaScript API
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
Java
JavaScript
Objective-C
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Java
Kotlin
Scala
Server-side scripts infoStored proceduresnoyes info.Net, JavaScript, C/C++limited functionality with using 'rules'Yes, possible languages: KQL, Python, Rno
TriggersnoyesCallbacks are triggered when data changesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioningSharding infoImplicit feature of the cloud servicenone
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesyes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Eventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Eventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanotunable from ACID to Eventually ConsistentyesnoAtomic execution of operations
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesYes, tunable from durable to delayed durability to in-memoryyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnoyes
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemFine grained access rights according to SQL-standard with additional protections for filesyes, based on authentication and database rulesAzure Active Directory Authenticationno

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More resources
Apache DruidFaircom DB infoformerly c-treeACEFirebase Realtime DatabaseMicrosoft Azure Data ExplorerSwayDB
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