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DBMS > Databricks vs. SiteWhere vs. Spark SQL

System Properties Comparison Databricks vs. SiteWhere vs. Spark SQL

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonSiteWhere  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionThe 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.M2M integration platform for persisting/querying time series dataSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument store
Relational DBMS
Time Series DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.06
Rank#356  Overall
#35  Time Series DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitewww.databricks.comgithub.com/­sitewhere/­sitewherespark.apache.org/­sql
Technical documentationdocs.databricks.comsitewhere1.sitewhere.io/­index.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperDatabricksSiteWhereApache Software Foundation
Initial release201320102014
Current release3.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoCommon Public Attribution License Version 1.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScala
Server operating systemshostedLinux
OS X
Windows
Linux
OS X
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)predefined schemeyes
Typing infopredefined data types such as float or dateyesyes
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.yesnono
Secondary indexesyesnono
SQL infoSupport of SQLwith Databricks SQLnoSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
HTTP RESTJDBC
ODBC
Supported programming languagesPython
R
Scala
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions and aggregatesno
Triggersno
Partitioning methods infoMethods for storing different data on different nodesSharding infobased on HBaseyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyesselectable replication factor infobased on HBasenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlUsers with fine-grained authorization conceptno
More information provided by the system vendor
DatabricksSiteWhereSpark SQL
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksSiteWhereSpark SQL
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