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DBMS > Databricks vs. Google Cloud Datastore vs. GraphDB

System Properties Comparison Databricks vs. Google Cloud Datastore vs. GraphDB

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonGraphDB infoformer name: OWLIM  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 PlatformEnterprise-ready RDF and graph database with efficient reasoning, cluster and external index synchronization support. It supports also SQL JDBC access to Knowledge Graph and GraphQL over SPARQL.
Primary database modelDocument store
Relational DBMS
Document storeGraph DBMS
RDF store
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
Score2.76
Rank#99  Overall
#8  Graph DBMS
#4  RDF stores
Websitewww.databricks.comcloud.google.com/­datastorewww.ontotext.com
Technical documentationdocs.databricks.comcloud.google.com/­datastore/­docsgraphdb.ontotext.com/­documentation
Social network pagesLinkedInTwitterYouTubeGitHubMedium
DeveloperDatabricksGoogleOntotext
Initial release201320082000
Current release10.4, October 2023
License infoCommercial or Open Sourcecommercialcommercialcommercial infoSome plugins of GraphDB Workbench are open sourced
Cloud-based only infoOnly available as a cloud serviceyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava
Server operating systemshostedhostedAll OS with a Java VM
Linux
OS X
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeschema-free and OWL/RDFS-schema support; RDF shapes
Typing infopredefined data types such as float or dateyes, details hereyes
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.yesnono
Secondary indexesyesyesyes, supports real-time synchronization and indexing in SOLR/Elastic search/Lucene and GeoSPARQL geometry data indexes
SQL infoSupport of SQLwith Databricks SQLSQL-like query language (GQL)stored SPARQL accessed as SQL using Apache Calcite through JDBC/ODBC
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
GeoSPARQL
GraphQL
GraphQL Federation
Java API
JDBC
RDF4J API
RDFS
RIO
Sail API
Sesame REST HTTP Protocol
SPARQL 1.1
Supported programming languagesPython
R
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C#
Clojure
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresuser defined functions and aggregatesusing Google App Enginewell-defined plugin interfaces; JavaScript server-side extensibility
TriggersCallbacks using the Google Apps Engineno
Partitioning methods infoMethods for storing different data on different nodesShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication using PaxosMulti-source replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowno
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, Eventual consistency (configurable in cluster mode per master or individual client request)
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsyes infoConstraint checking
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID
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 controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Default Basic authentication through RDF4J client, or via Java when run with cURL, default token-based in the Workbench or via Rest API, optional access through OpenID or Kerberos single sign-on.
More information provided by the system vendor
DatabricksGoogle Cloud DatastoreGraphDB infoformer name: OWLIM
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
» more
Ontotext GraphDB is a semantic database engine that allows organizations to build...
» more
Competitive advantagesGraphDB allows you to link text and data in big knowledge graphs. It’s easy to experiment...
» more
Typical application scenariosMetadata enrichment and management, linked data publishing, semantic inferencing...
» more
Key customers​ GraphDB provides a platform for building next-generation AI and Knowledge Graph...
» more
Market metricsGraphDB is the most utilized semantic triplestore for mission-critical enterprise...
» more
Licensing and pricing modelsGraphDB Free is a non-commercial version and is free to use. GraphDB Enterprise edition...
» more
News

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The Power of Knowledge Graphs – A Chat with Perfect Memory and Ontotext
16 August 2024

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
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