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DBMS > Firebase Realtime Database vs. Hawkular Metrics vs. MarkLogic vs. Splice Machine

System Properties Comparison Firebase Realtime Database vs. Hawkular Metrics vs. MarkLogic vs. Splice Machine

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Editorial information provided by DB-Engines
NameFirebase Realtime Database  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonMarkLogic  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Operational and transactional Enterprise NoSQL databaseOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelDocument storeTime Series DBMSDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score18.02
Rank#38  Overall
#5  Document stores
Score0.06
Rank#324  Overall
#29  Time Series DBMS
Score9.60
Rank#49  Overall
#7  Document stores
#1  Native XML DBMS
#1  RDF stores
#4  Search engines
Score0.75
Rank#201  Overall
#98  Relational DBMS
Websitefirebase.google.com/­products/­realtime-databasewww.hawkular.orgwww.marklogic.comsplicemachine.com
Technical documentationfirebase.google.com/­docs/­databasewww.hawkular.org/­hawkular-metrics/­docs/­user-guidedocs.marklogic.comdoc.splicemachine.com
DeveloperGoogle infoacquired by Google 2014Community supported by Red HatMarkLogic Corp.Splice Machine
Initial release2012201420012014
Current release9.0, 20173.1, March 2021
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercial inforestricted free version is availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++Java
Server operating systemshostedLinux
OS X
Windows
Linux
OS X
Windows
Linux
OS X
Solaris
Windows
Data schemeschema-freeschema-freeschema-free infoSchema can be enforcedyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.nonoyes
Secondary indexesyesnoyesyes
SQL infoSupport of SQLnonoyes infoSQL92yes
APIs and other access methodsAndroid
iOS
JavaScript API
RESTful HTTP API
HTTP RESTJava API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
JDBC
Native Spark Datasource
ODBC
Supported programming languagesJava
JavaScript
Objective-C
Go
Java
Python
Ruby
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored procedureslimited functionality with using 'rules'noyes infovia XQuery or JavaScriptyes infoJava
TriggersCallbacks are triggered when data changesyes infovia Hawkular Alertingyesyes
Partitioning methods infoMethods for storing different data on different nodesSharding infobased on CassandraShardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor infobased on CassandrayesMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Eventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesnoACID infocan act as a resource manager in an XA/JTA transactionACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes, with Range Indexesyes
User concepts infoAccess controlyes, based on authentication and database rulesnoRole-based access control at the document and subdocument levelsAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
Firebase Realtime DatabaseHawkular MetricsMarkLogicSplice Machine
Specific characteristicsThe MarkLogic Multi-Model Database provides the simplest way to integrate data from...
» more
Competitive advantagesThe main benefit of MarkLogic is simplicity. By making things simpler, MarkLogic...
» more
Typical application scenariosMarkLogic’s main use case is the Operational Data Hub . A Data Hub is useful for...
» more
Key customersMarkLogic has proven results managing mission-critical data with over 1,000 global...
» more
Market metricsMarkLogic is well known within the Fortune 1000, with a strong presence in financial...
» more
Licensing and pricing modelsMarkLogic provides a variety of licenses and cloud services: MarkLogic Data Hub Service...
» more
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More resources
Firebase Realtime DatabaseHawkular MetricsMarkLogicSplice Machine
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