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DBMS > Databricks vs. FatDB vs. Splice Machine vs. Stardog

System Properties Comparison Databricks vs. FatDB vs. Splice Machine vs. Stardog

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonFatDB  Xexclude from comparisonSplice Machine  Xexclude from comparisonStardog  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
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.A .NET NoSQL DBMS that can integrate with and extend SQL Server.Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and SparkEnterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization
Primary database modelDocument store
Relational DBMS
Document store
Key-value store
Relational DBMSGraph DBMS
RDF 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
Score0.54
Rank#250  Overall
#114  Relational DBMS
Score2.02
Rank#123  Overall
#11  Graph DBMS
#6  RDF stores
Websitewww.databricks.comsplicemachine.comwww.stardog.com
Technical documentationdocs.databricks.comsplicemachine.com/­how-it-worksdocs.stardog.com
DeveloperDatabricksFatCloudSplice MachineStardog-Union
Initial release2013201220142010
Current release3.1, March 20217.3.0, May 2020
License infoCommercial or Open SourcecommercialcommercialOpen Source infoAGPL 3.0, commercial license availablecommercial info60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC#JavaJava
Server operating systemshostedWindowsLinux
OS X
Solaris
Windows
Linux
macOS
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyesschema-free and OWL/RDFS-schema support
Typing infopredefined data types such as float or dateyesyesyes
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.yesno infoImport/export of XML data possible
Secondary indexesyesyesyesyes infosupports real-time indexing in full-text and geospatial
SQL infoSupport of SQLwith Databricks SQLno infoVia inetgration in SQL ServeryesYes, compatible with all major SQL variants through dedicated BI/SQL Server
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
JDBC
Native Spark Datasource
ODBC
GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3
Supported programming languagesPython
R
Scala
C#C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes infovia applicationsyes infoJavauser defined functions and aggregates, HTTP Server extensions in Java
Triggersyes infovia applicationsyesyes infovia event handlers
Partitioning methods infoMethods for storing different data on different nodesShardingShared Nothhing Auto-Sharding, Columnar Partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesselectable replication factorMulti-source replication
Source-replica replication
Multi-source replication in HA-Cluster
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesYes, via Full Spark Integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency in HA-Cluster
Foreign keys infoReferential integritynoyesyes inforelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)yes
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.noyesyes
User concepts infoAccess controlno infoCan implement custom security layer via applicationsAccess rights for users, groups and roles according to SQL-standardAccess rights for users and roles
More information provided by the system vendor
DatabricksFatDBSplice MachineStardog
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksFatDBSplice MachineStardog
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