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DBMS > Databricks vs. Kingbase vs. Spark SQL

System Properties Comparison Databricks vs. Kingbase vs. Spark SQL

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonKingbase  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.An enterprise-class RDBMS compatible with PostgreSQL and Oracle and widely used in China.Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument store
Relational DBMS
Relational DBMSRelational DBMS
Secondary database modelsDocument store
Spatial 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
Score0.45
Rank#262  Overall
#123  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitewww.databricks.comwww.kingbase.com.cnspark.apache.org/­sql
Technical documentationdocs.databricks.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperDatabricksBeiJing KINGBASE Information technologies inc.Apache Software Foundation
Initial release201319992014
Current releaseV8.0, August 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialcommercialOpen 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 languageC and JavaScala
Server operating systemshostedLinux
Windows
Linux
OS X
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyes
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.yesyesno
Secondary indexesyesyesno
SQL infoSupport of SQLwith Databricks SQLStandard with numerous extensionsSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
ADO.NET
gokb
JDBC
kdbndp
ODBC
PDI
PDO
Pro*C
psycopg2
QT
JDBC
ODBC
Supported programming languagesPython
R
Scala
.Net
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions and aggregatesuser defined functionsno
Triggersyesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by range, list and hash) and vertical partitioningyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyesyesnone
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 integrityyesno
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 persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlfine grained access rights according to SQL-standardno
More information provided by the system vendor
DatabricksKingbaseSpark SQL
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksKingbaseSpark SQL
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