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DBMS > Databricks vs. Google Cloud Datastore vs. Hawkular Metrics vs. RDF4J vs. STSdb

System Properties Comparison Databricks vs. Google Cloud Datastore vs. Hawkular Metrics vs. RDF4J vs. STSdb

Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonSTSdb  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 PlatformHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.Key-Value Store with special method for indexing infooptimized for high performance using a special indexing method
Primary database modelDocument store
Relational DBMS
Document storeTime Series DBMSRDF storeKey-value store
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
Score0.00
Rank#379  Overall
#40  Time Series DBMS
Score0.69
Rank#230  Overall
#9  RDF stores
Score0.04
Rank#360  Overall
#52  Key-value stores
Websitewww.databricks.comcloud.google.com/­datastorewww.hawkular.orgrdf4j.orggithub.com/­STSSoft/­STSdb4
Technical documentationdocs.databricks.comcloud.google.com/­datastore/­docswww.hawkular.org/­hawkular-metrics/­docs/­user-guiderdf4j.org/­documentation
DeveloperDatabricksGoogleCommunity supported by Red HatSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.STS Soft SC
Initial release20132008201420042011
Current release4.0.8, September 2015
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0Open Source infoEclipse Distribution License (EDL), v1.0.Open Source infoGPLv2, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaC#
Server operating systemshostedhostedLinux
OS X
Windows
Linux
OS X
Unix
Windows
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeschema-freeyes infoRDF Schemasyes
Typing infopredefined data types such as float or dateyes, details hereyesyesyes infoprimitive types and user defined types (classes)
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 indexesyesyesnoyesno
SQL infoSupport of SQLwith Databricks SQLSQL-like query language (GQL)nonono
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
HTTP RESTJava API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
.NET Client API
Supported programming languagesPython
R
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Go
Java
Python
Ruby
Java
PHP
Python
C#
Java
Server-side scripts infoStored proceduresuser defined functions and aggregatesusing Google App Enginenoyesno
TriggersCallbacks using the Google Apps Engineyes infovia Hawkular Alertingyesno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infobased on Cassandranonenone
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication using Paxosselectable replication factor infobased on Cassandranonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownonono
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 Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACID infoIsolation support depends on the API usedno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoin-memory storage is supported as wellyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nonono
More information provided by the system vendor
DatabricksGoogle Cloud DatastoreHawkular MetricsRDF4J infoformerly known as SesameSTSdb
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksGoogle Cloud DatastoreHawkular MetricsRDF4J infoformerly known as SesameSTSdb
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