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DBMS > Apache IoTDB vs. Databricks vs. Hypertable vs. Infobright vs. JanusGraph

System Properties Comparison Apache IoTDB vs. Databricks vs. Hypertable vs. Infobright vs. JanusGraph

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonDatabricks  Xexclude from comparisonHypertable  Xexclude from comparisonInfobright  Xexclude from comparisonJanusGraph infosuccessor of Titan  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
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.An open source BigTable implementation based on distributed file systems such as HadoopHigh performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontendA Graph DBMS optimized for distributed clusters infoIt was forked from the latest code base of Titan in January 2017
Primary database modelTime Series DBMSDocument store
Relational DBMS
Wide column storeRelational DBMSGraph DBMS
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
Score0.96
Rank#194  Overall
#91  Relational DBMS
Score1.94
Rank#129  Overall
#12  Graph DBMS
Websiteiotdb.apache.orgwww.databricks.comignitetech.com/­softwarelibrary/­infobrightdbjanusgraph.org
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.databricks.comdocs.janusgraph.org
DeveloperApache Software FoundationDatabricksHypertable Inc.Ignite Technologies Inc.; formerly InfoBright Inc.Linux Foundation; originally developed as Titan by Aurelius
Initial release20182013200920052017
Current release1.1.0, April 20230.9.8.11, March 20160.6.3, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoGNU version 3. Commercial license availablecommercial infoThe open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++CJava
Server operating systemsAll OS with a Java VM (>= 1.8)hostedLinux
OS X
Windows infoan inofficial Windows port is available
Linux
Windows
Linux
OS X
Unix
Windows
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)schema-freeyesyes
Typing infopredefined data types such as float or dateyesnoyesyes
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.noyesnono
Secondary indexesyesyesrestricted infoonly exact value or prefix value scansno infoKnowledge Grid Technology used insteadyes
SQL infoSupport of SQLSQL-like query languagewith Databricks SQLnoyesno
APIs and other access methodsJDBC
Native API
JDBC
ODBC
RESTful HTTP API
C++ API
Thrift
ADO.NET
JDBC
ODBC
Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Python
R
Scala
C++
Java
Perl
PHP
Python
Ruby
.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Clojure
Java
Python
Server-side scripts infoStored proceduresyesuser defined functions and aggregatesnonoyes
Triggersyesnonoyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Shardingnoneyes infodepending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasyesselectable replication factor on file system levelSource-replica replicationyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyesnoyes infovia Faunus, a graph analytics engine
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynononoyes infoRelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes infoSupports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes
User concepts infoAccess controlyesnofine grained access rights according to SQL-standard infoexploiting MySQL or PostgreSQL frontend capabilitiesUser authentification and security via Rexster Graph Server
More information provided by the system vendor
Apache IoTDBDatabricksHypertableInfobrightJanusGraph infosuccessor of Titan
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Apache IoTDBDatabricksHypertableInfobrightJanusGraph infosuccessor of Titan
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