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DBMS > Databricks vs. Google Cloud Datastore vs. IBM Cloudant vs. Kinetica

System Properties Comparison Databricks vs. Google Cloud Datastore vs. IBM Cloudant vs. Kinetica

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonIBM Cloudant  Xexclude from comparisonKinetica  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 PlatformDatabase as a Service offering based on Apache CouchDBFully vectorized database across both GPUs and CPUs
Primary database modelDocument store
Relational DBMS
Document storeDocument storeRelational DBMS
Secondary database modelsSpatial DBMS
Time Series 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
Score4.47
Rank#76  Overall
#12  Document stores
Score2.68
Rank#106  Overall
#20  Document stores
Score0.64
Rank#236  Overall
#109  Relational DBMS
Websitewww.databricks.comcloud.google.com/­datastorewww.ibm.com/­products/­cloudantwww.kinetica.com
Technical documentationdocs.databricks.comcloud.google.com/­datastore/­docscloud.ibm.com/­docs/­Cloudantdocs.kinetica.com
DeveloperDatabricksGoogleIBM, Apache Software Foundation infoIBM acquired Cloudant in February 2014Kinetica
Initial release2013200820102012
Current release7.1, August 2021
License infoCommercial or Open Sourcecommercialcommercialcommercialcommercial
Cloud-based only infoOnly available as a cloud serviceyesyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageErlangC, C++
Server operating systemshostedhostedhostedLinux
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeschema-freeyes
Typing infopredefined data types such as float or dateyes, details herenoyes
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 indexesyesyesyesyes
SQL infoSupport of SQLwith Databricks SQLSQL-like query language (GQL)noSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
RESTful HTTP/JSON APIJDBC
ODBC
RESTful HTTP API
Supported programming languagesPython
R
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
Java
JavaScript
Objective-C
PHP
Ruby
C++
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresuser defined functions and aggregatesusing Google App EngineView functions (Map-Reduce) in JavaScriptuser defined functions
TriggersCallbacks using the Google Apps Engineyesyes infotriggers when inserted values for one or more columns fall within a specified range
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication using PaxosMulti-source replication
Source-replica replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowyesno
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.Eventual ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsno infoatomic operations within a document possibleno
Concurrency infoSupport for concurrent manipulation of datayesyesyes infoOptimistic lockingyes
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.nononoyes 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 can be defined per databaseAccess rights for users and roles on table level
More information provided by the system vendor
DatabricksGoogle Cloud DatastoreIBM CloudantKinetica
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksGoogle Cloud DatastoreIBM CloudantKinetica
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