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DBMS > Databricks vs. Google Cloud Datastore vs. Kinetica vs. Kingbase

System Properties Comparison Databricks vs. Google Cloud Datastore vs. Kinetica vs. Kingbase

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonKinetica  Xexclude from comparisonKingbase  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.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformFully vectorized database across both GPUs and CPUsAn enterprise-class RDBMS compatible with PostgreSQL and Oracle and widely used in China.
Primary database modelDocument store
Relational DBMS
Document storeRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Document store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score84.24
Rank#14  Overall
#2  Document stores
#9  Relational DBMS
Score4.13
Rank#71  Overall
#12  Document stores
Score0.42
Rank#261  Overall
#120  Relational DBMS
Score0.44
Rank#258  Overall
#117  Relational DBMS
Websitewww.databricks.comcloud.google.com/­datastorewww.kinetica.comwww.kingbase.com.cn
Technical documentationdocs.databricks.comcloud.google.com/­datastore/­docsdocs.kinetica.com
DeveloperDatabricksGoogleKineticaBeiJing KINGBASE Information technologies inc.
Initial release2013200820121999
Current release7.1, August 2021V8.0, August 2021
License infoCommercial or Open Sourcecommercialcommercialcommercialcommercial
Cloud-based only infoOnly available as a cloud serviceyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++C and Java
Server operating systemshostedhostedLinuxLinux
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyesyes
Typing infopredefined data types such as float or dateyes, details hereyesyes
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.yesnonoyes
Secondary indexesyesyesyesyes
SQL infoSupport of SQLwith Databricks SQLSQL-like query language (GQL)SQL-like DML and DDL statementsStandard with numerous extensions
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
RESTful HTTP API
ADO.NET
gokb
JDBC
kdbndp
ODBC
PDI
PDO
Pro*C
psycopg2
QT
Supported programming languagesPython
R
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++
Java
JavaScript (Node.js)
Python
.Net
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Server-side scripts infoStored proceduresuser defined functions and aggregatesusing Google App Engineuser defined functionsuser defined functions
TriggersCallbacks using the Google Apps Engineyes infotriggers when inserted values for one or more columns fall within a specified rangeyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardinghorizontal partitioning (by range, list and hash) and vertical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication using PaxosSource-replica replicationyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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 infoGPU vRAM or System RAM
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users and roles on table levelfine grained access rights according to SQL-standard
More information provided by the system vendor
DatabricksGoogle Cloud DatastoreKineticaKingbase
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksGoogle Cloud DatastoreKineticaKingbase
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