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DBMS > Apache IoTDB vs. Databricks vs. Hazelcast vs. IBM Cloudant vs. Splice Machine

System Properties Comparison Apache IoTDB vs. Databricks vs. Hazelcast vs. IBM Cloudant vs. Splice Machine

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonDatabricks  Xexclude from comparisonHazelcast  Xexclude from comparisonIBM Cloudant  Xexclude from comparisonSplice Machine  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 FlinkThe 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.A widely adopted in-memory data gridDatabase as a Service offering based on Apache CouchDBOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelTime Series DBMSDocument store
Relational DBMS
Key-value storeDocument storeRelational DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score5.46
Rank#61  Overall
#7  Key-value stores
Score2.75
Rank#104  Overall
#19  Document stores
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websiteiotdb.apache.orgwww.databricks.comhazelcast.comwww.ibm.com/­products/­cloudantsplicemachine.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.databricks.comhazelcast.org/­imdg/­docscloud.ibm.com/­docs/­Cloudantsplicemachine.com/­how-it-works
DeveloperApache Software FoundationDatabricksHazelcastIBM, Apache Software Foundation infoIBM acquired Cloudant in February 2014Splice Machine
Initial release20182013200820102014
Current release1.1.0, April 20235.3.6, November 20233.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2; commercial licenses availablecommercialOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenoyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaErlangJava
Server operating systemsAll OS with a Java VM (>= 1.8)hostedAll OS with a Java VMhostedLinux
OS X
Solaris
Windows
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)schema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyesnoyes
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.noyesyes infothe object must implement a serialization strategyno
Secondary indexesyesyesyesyesyes
SQL infoSupport of SQLSQL-like query languagewith Databricks SQLSQL-like query languagenoyes
APIs and other access methodsJDBC
Native API
JDBC
ODBC
RESTful HTTP API
JCache
JPA
Memcached protocol
RESTful HTTP API
RESTful HTTP/JSON APIJDBC
Native Spark Datasource
ODBC
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Python
R
Scala
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C#
Java
JavaScript
Objective-C
PHP
Ruby
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyesuser defined functions and aggregatesyes infoEvent Listeners, Executor ServicesView functions (Map-Reduce) in JavaScriptyes infoJava
Triggersyesyes infoEventsyesyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingShardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasyesyes infoReplicated MapMulti-source replication
Source-replica replication
Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparkyesyesYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDone or two-phase-commit; repeatable reads; read commitedno infoatomic operations within a document possibleACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infoOptimistic lockingyes, multi-version concurrency control (MVCC)
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.yesnoyesnoyes
User concepts infoAccess controlyesRole-based access controlAccess rights for users can be defined per databaseAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
Apache IoTDBDatabricksHazelcastIBM CloudantSplice Machine
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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Apache IoTDBDatabricksHazelcastIBM CloudantSplice Machine
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