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DBMS > Databricks vs. Kyligence Enterprise vs. Realm vs. Spark SQL

System Properties Comparison Databricks vs. Kyligence Enterprise vs. Realm vs. Spark SQL

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonKyligence Enterprise  Xexclude from comparisonRealm  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.A distributed analytics engine for big data, built on top of Apache KylinA DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core DataSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument store
Relational DBMS
Relational DBMSDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.46
Rank#266  Overall
#124  Relational DBMS
Score7.41
Rank#52  Overall
#8  Document stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitewww.databricks.comkyligence.io/­kyligence-enterpriserealm.iospark.apache.org/­sql
Technical documentationdocs.databricks.comrealm.io/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperDatabricksKyligence, Inc.Realm, acquired by MongoDB in May 2019Apache Software Foundation
Initial release2013201620142014
Current release3.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialcommercialOpen SourceOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScala
Server operating systemshostedLinuxAndroid
Backend: server-less
iOS
Windows
Linux
OS X
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyesyes
Typing infopredefined data types such as float or dateyesyesyes
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.yesnonono
Secondary indexesyesyesyesno
SQL infoSupport of SQLwith Databricks SQLANSI SQL for queries (using Apache Calcite)noSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Supported programming languagesPython
R
Scala
.Net
Java infowith Android only
Objective-C
React Native
Swift
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions and aggregatesno inforuns within the applications so server-side scripts are unnecessaryno
Triggersyes infoChange Listenersno
Partitioning methods infoMethods for storing different data on different nodesnoneyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes infoIn-Memory realmno
User concepts infoAccess controlyesno
More information provided by the system vendor
DatabricksKyligence EnterpriseRealmSpark SQL
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksKyligence EnterpriseRealmSpark SQL
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