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DBMS > Apache IoTDB vs. Databricks vs. Graph Engine vs. Hazelcast vs. HBase

System Properties Comparison Apache IoTDB vs. Databricks vs. Graph Engine vs. Hazelcast vs. HBase

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonDatabricks  Xexclude from comparisonGraph Engine infoformer name: Trinity  Xexclude from comparisonHazelcast  Xexclude from comparisonHBase  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 distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engineA widely adopted in-memory data gridWide-column store based on Apache Hadoop and on concepts of BigTable
Primary database modelTime Series DBMSDocument store
Relational DBMS
Graph DBMS
Key-value store
Key-value storeWide column store
Secondary database modelsDocument 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
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.61
Rank#240  Overall
#21  Graph DBMS
#35  Key-value stores
Score5.97
Rank#57  Overall
#6  Key-value stores
Score30.50
Rank#26  Overall
#2  Wide column stores
Websiteiotdb.apache.orgwww.databricks.comwww.graphengine.iohazelcast.comhbase.apache.org
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.databricks.comwww.graphengine.io/­docs/­manualhazelcast.org/­imdg/­docshbase.apache.org/­book.html
DeveloperApache Software FoundationDatabricksMicrosoftHazelcastApache Software Foundation infoApache top-level project, originally developed by Powerset
Initial release20182013201020082008
Current release1.1.0, April 20235.3.6, November 20232.3.4, January 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoMIT LicenseOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache version 2
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava.NET and CJavaJava
Server operating systemsAll OS with a Java VM (>= 1.8)hosted.NETAll OS with a Java VMLinux
Unix
Windows infousing Cygwin
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)yesschema-freeschema-free, schema definition possible
Typing infopredefined data types such as float or dateyesyesyesoptions to bring your own types, AVRO
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.noyesnoyes infothe object must implement a serialization strategyno
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL-like query languagewith Databricks SQLnoSQL-like query languageno
APIs and other access methodsJDBC
Native API
JDBC
ODBC
RESTful HTTP API
RESTful HTTP APIJCache
JPA
Memcached protocol
RESTful HTTP API
Java API
RESTful HTTP API
Thrift
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Python
R
Scala
C#
C++
F#
Visual Basic
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C
C#
C++
Groovy
Java
PHP
Python
Scala
Server-side scripts infoStored proceduresyesuser defined functions and aggregatesyesyes infoEvent Listeners, Executor Servicesyes infoCoprocessors in Java
Triggersyesnoyes infoEventsyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)horizontal partitioningShardingSharding
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
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyesyes
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 AlgorithmImmediate Consistency or Eventual Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoone or two-phase-commit; repeatable reads; read commitedSingle row ACID (across millions of columns)
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesoptional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storageyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesyesyes
User concepts infoAccess controlyesRole-based access controlAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC
More information provided by the system vendor
Apache IoTDBDatabricksGraph Engine infoformer name: TrinityHazelcastHBase
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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Apache IoTDBDatabricksGraph Engine infoformer name: TrinityHazelcastHBase
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