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DBMS > BigObject vs. eXtremeDB vs. Firebase Realtime Database vs. Heroic vs. Microsoft Azure Data Explorer

System Properties Comparison BigObject vs. eXtremeDB vs. Firebase Realtime Database vs. Heroic vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameBigObject  Xexclude from comparisoneXtremeDB  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonHeroic  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionAnalytic DBMS for real-time computations and queriesNatively in-memory DBMS with options for persistency, high-availability and clusteringCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchFully managed big data interactive analytics platform
Primary database modelRelational DBMS infoa hierachical model (tree) can be imposedRelational DBMS
Time Series DBMS
Document storeTime Series DBMSRelational DBMS infocolumn oriented
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
Score0.13
Rank#333  Overall
#147  Relational DBMS
Score0.74
Rank#223  Overall
#103  Relational DBMS
#18  Time Series DBMS
Score14.29
Rank#39  Overall
#6  Document stores
Score0.51
Rank#255  Overall
#21  Time Series DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websitebigobject.iowww.mcobject.comfirebase.google.com/­products/­realtime-databasegithub.com/­spotify/­heroicazure.microsoft.com/­services/­data-explorer
Technical documentationdocs.bigobject.iowww.mcobject.com/­docs/­extremedb.htmfirebase.google.com/­docs/­databasespotify.github.io/­heroicdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperBigObject, Inc.McObjectGoogle infoacquired by Google 2014SpotifyMicrosoft
Initial release20152001201220142019
Current release8.2, 2021cloud service with continuous releases
License infoCommercial or Open Sourcecommercial infofree community edition availablecommercialcommercialOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenonoyesnoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC and C++Java
Server operating systemsLinux infodistributed as a docker-image
OS X infodistributed as a docker-image (boot2docker)
Windows infodistributed as a docker-image (boot2docker)
AIX
HP-UX
Linux
macOS
Solaris
Windows
hostedhosted
Data schemeyesyesschema-freeschema-freeFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyesyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-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.nono infosupport of XML interfaces availablenonoyes
Secondary indexesyesyesyesyes infovia Elasticsearchall fields are automatically indexed
SQL infoSupport of SQLSQL-like DML and DDL statementsyes infowith the option: eXtremeSQLnonoKusto Query Language (KQL), SQL subset
APIs and other access methodsfluentd
ODBC
RESTful HTTP API
.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API
Android
iOS
JavaScript API
RESTful HTTP API
HQL (Heroic Query Language, a JSON-based language)
HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languages.Net
C
C#
C++
Java
Lua
Python
Scala
Java
JavaScript
Objective-C
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresLuayeslimited functionality with using 'rules'noYes, possible languages: KQL, Python, R
Triggersnoyes infoby defining eventsCallbacks are triggered when data changesnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesnonehorizontal partitioning / shardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesnoneActive Replication Fabricâ„¢ for IoT
Multi-source replication infoby means of eXtremeDB Cluster option
Source-replica replication infoby means of eXtremeDB High Availability option
yesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonononoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Eventual Consistency
Immediate Consistency
Eventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyes infoautomatically between fact table and dimension tablesyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDyesnono
Concurrency infoSupport for concurrent manipulation of datayes infoRead/write lock on objects (tables, trees)yes infoOptimistic (MVCC) and pessimistic (locking) strategies availableyesyesyes
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.yesyesnono
User concepts infoAccess controlnoyes, based on authentication and database rulesAzure Active Directory Authentication
More information provided by the system vendor
BigObjecteXtremeDBFirebase Realtime DatabaseHeroicMicrosoft Azure Data Explorer
Specific characteristicseXtremeDB is an in-memory and/or persistent database system that offers an ultra-small...
» more
Competitive advantageseXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
» more
Typical application scenariosIoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
» more
Key customersSchneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
» more
Market metricsWith hundreds of customers and over 30 million devices/applications using the product...
» more
Licensing and pricing modelsFor server use cases, there is a simple per-server license irrespective of the number...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
BigObjecteXtremeDBFirebase Realtime DatabaseHeroicMicrosoft Azure Data Explorer
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