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DBMS > Amazon Aurora vs. Databricks vs. Google Cloud Datastore vs. RDF4J

System Properties Comparison Amazon Aurora vs. Databricks vs. Google Cloud Datastore vs. RDF4J

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Editorial information provided by DB-Engines
NameAmazon Aurora  Xexclude from comparisonDatabricks  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparison
DescriptionMySQL and PostgreSQL compatible cloud service by AmazonThe 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 PlatformRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.
Primary database modelRelational DBMSDocument store
Relational DBMS
Document storeRDF store
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.84
Rank#44  Overall
#28  Relational DBMS
Score84.24
Rank#14  Overall
#2  Document stores
#9  Relational DBMS
Score4.13
Rank#71  Overall
#12  Document stores
Score0.72
Rank#222  Overall
#9  RDF stores
Websiteaws.amazon.com/­rds/­aurorawww.databricks.comcloud.google.com/­datastorerdf4j.org
Technical documentationdocs.aws.amazon.com/­AmazonRDS/­latest/­AuroraUserGuide/­CHAP_Aurora.htmldocs.databricks.comcloud.google.com/­datastore/­docsrdf4j.org/­documentation
DeveloperAmazonDatabricksGoogleSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release2015201320082004
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoEclipse Distribution License (EDL), v1.0.
Cloud-based only infoOnly available as a cloud serviceyesyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava
Server operating systemshostedhostedhostedLinux
OS X
Unix
Windows
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)schema-freeyes infoRDF Schemas
Typing infopredefined data types such as float or dateyesyes, 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.yesyesno
Secondary indexesyesyesyesyes
SQL infoSupport of SQLyeswith Databricks SQLSQL-like query language (GQL)no
APIs and other access methodsADO.NET
JDBC
ODBC
JDBC
ODBC
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Supported programming languagesAda
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Python
R
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
PHP
Python
Server-side scripts infoStored proceduresyesuser defined functions and aggregatesusing Google App Engineyes
TriggersyesCallbacks using the Google Apps Engineyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyesMulti-source replication using Paxosnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate 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.
Foreign keys infoReferential integrityyesyes infovia ReferenceProperties or Ancestor paths
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID infoIsolation support depends on the API used
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoin-memory storage is supported as well
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnono
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)no
More information provided by the system vendor
Amazon AuroraDatabricksGoogle Cloud DatastoreRDF4J infoformerly known as Sesame
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Amazon AuroraDatabricksGoogle Cloud DatastoreRDF4J infoformerly known as Sesame
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