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

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

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonKinetica  Xexclude from comparisonXTDB infoformerly named Crux  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 CPUsA general purpose database with bitemporal SQL and Datalog and graph queries
Primary database modelDocument store
Relational DBMS
Document storeRelational DBMSDocument store
Secondary database modelsSpatial DBMS
Time Series 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
Score4.36
Rank#72  Overall
#12  Document stores
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score0.18
Rank#332  Overall
#46  Document stores
Websitewww.databricks.comcloud.google.com/­datastorewww.kinetica.comgithub.com/­xtdb/­xtdb
www.xtdb.com
Technical documentationdocs.databricks.comcloud.google.com/­datastore/­docsdocs.kinetica.comwww.xtdb.com/­docs
DeveloperDatabricksGoogleKineticaJuxt Ltd.
Initial release2013200820122019
Current release7.1, August 20211.19, September 2021
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoMIT License
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++Clojure
Server operating systemshostedhostedLinuxAll OS with a Java 8 (and higher) VM
Linux
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyesschema-free
Typing infopredefined data types such as float or dateyes, details hereyesyes, extensible-data-notation format
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)SQL-like DML and DDL statementslimited SQL, making use of Apache Calcite
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
RESTful HTTP API
HTTP REST
JDBC
Supported programming languagesPython
R
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++
Java
JavaScript (Node.js)
Python
Clojure
Java
Server-side scripts infoStored proceduresuser defined functions and aggregatesusing Google App Engineuser defined functionsno
TriggersCallbacks using the Google Apps Engineyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication using PaxosSource-replica replicationyes, each node contains all data
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 configuration
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsyesno
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, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB
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 level
More information provided by the system vendor
DatabricksGoogle Cloud DatastoreKineticaXTDB infoformerly named Crux
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksGoogle Cloud DatastoreKineticaXTDB infoformerly named Crux
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