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DBMS > Apache IoTDB vs. Citus vs. Databricks vs. Firebase Realtime Database vs. Hazelcast

System Properties Comparison Apache IoTDB vs. Citus vs. Databricks vs. Firebase Realtime Database vs. Hazelcast

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonCitus  Xexclude from comparisonDatabricks  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonHazelcast  Xexclude from comparison
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.A widely adopted in-memory data grid
Primary database modelTime Series DBMSRelational DBMSDocument store
Relational DBMS
Document storeKey-value store
Secondary database modelsDocument storeDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score2.21
Rank#118  Overall
#56  Relational DBMS
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score14.29
Rank#39  Overall
#6  Document stores
Score5.97
Rank#57  Overall
#6  Key-value stores
Websiteiotdb.apache.orgwww.citusdata.comwww.databricks.comfirebase.google.com/­products/­realtime-databasehazelcast.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.citusdata.comdocs.databricks.comfirebase.google.com/­docs/­databasehazelcast.org/­imdg/­docs
DeveloperApache Software FoundationDatabricksGoogle infoacquired by Google 2014Hazelcast
Initial release20182010201320122008
Current release1.1.0, April 20238.1, December 20185.3.6, November 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoAGPL, commercial license also availablecommercialcommercialOpen Source infoApache Version 2; commercial licenses available
Cloud-based only infoOnly available as a cloud servicenonoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaCJava
Server operating systemsAll OS with a Java VM (>= 1.8)LinuxhostedhostedAll OS with a Java VM
Data schemeyesyesFlexible Schema (defined schema, partial schema, schema free)schema-freeschema-free
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.noyes infospecific XML type available, but no XML query functionalityyesnoyes infothe object must implement a serialization strategy
Secondary indexesyesyesyesyesyes
SQL infoSupport of SQLSQL-like query languageyes infostandard, with numerous extensionswith Databricks SQLnoSQL-like query language
APIs and other access methodsJDBC
Native API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
RESTful HTTP API
Android
iOS
JavaScript API
RESTful HTTP API
JCache
JPA
Memcached protocol
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Python
R
Scala
Java
JavaScript
Objective-C
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
Server-side scripts infoStored proceduresyesuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.user defined functions and aggregateslimited functionality with using 'rules'yes infoEvent Listeners, Executor Services
TriggersyesyesCallbacks are triggered when data changesyes infoEvents
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasSource-replica replication infoother methods possible by using 3rd party extensionsyesyes infoReplicated Map
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknonoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate ConsistencyEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Immediate Consistency or Eventual Consistency selectable by user infoRaft Consensus Algorithm
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDyesone or two-phase-commit; repeatable reads; read commited
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
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.yesnonoyes
User concepts infoAccess controlyesfine grained access rights according to SQL-standardyes, based on authentication and database rulesRole-based access control
More information provided by the system vendor
Apache IoTDBCitusDatabricksFirebase Realtime DatabaseHazelcast
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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Apache IoTDBCitusDatabricksFirebase Realtime DatabaseHazelcast
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