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DBMS > Apache IoTDB vs. Databricks

System Properties Comparison Apache IoTDB vs. Databricks

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonDatabricks  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.
Primary database modelTime Series DBMSDocument store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.19
Rank#176  Overall
#15  Time Series DBMS
Score76.33
Rank#17  Overall
#3  Document stores
#11  Relational DBMS
Websiteiotdb.apache.orgwww.databricks.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.databricks.com
DeveloperApache Software FoundationDatabricks
Initial release20182013
Current release1.1.0, April 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial
Cloud-based only infoOnly available as a cloud servicenoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava
Server operating systemsAll OS with a Java VM (>= 1.8)hosted
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)
Typing infopredefined data types such as float or dateyes
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
Secondary indexesyesyes
SQL infoSupport of SQLSQL-like query languagewith Databricks SQL
APIs and other access methodsJDBC
Native API
JDBC
ODBC
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Python
R
Scala
Server-side scripts infoStored proceduresyesuser defined functions and aggregates
Triggersyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Spark
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate Consistency
Foreign keys infoReferential integrityno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesno
User concepts infoAccess controlyes
More information provided by the system vendor
Apache IoTDBDatabricks
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Apache IoTDBDatabricks
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