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DBMS > Databricks vs. Microsoft Azure Table Storage vs. Netezza vs. Stardog

System Properties Comparison Databricks vs. Microsoft Azure Table Storage vs. Netezza vs. Stardog

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonStardog  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.A Wide Column Store for rapid development using massive semi-structured datasetsData warehouse and analytics appliance part of IBM PureSystemsEnterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization
Primary database modelDocument store
Relational DBMS
Wide column storeRelational 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
Score4.48
Rank#75  Overall
#6  Wide column stores
Score9.06
Rank#46  Overall
#29  Relational DBMS
Score2.02
Rank#123  Overall
#11  Graph DBMS
#6  RDF stores
Websitewww.databricks.comazure.microsoft.com/­en-us/­services/­storage/­tableswww.ibm.com/­products/­netezzawww.stardog.com
Technical documentationdocs.databricks.comdocs.stardog.com
DeveloperDatabricksMicrosoftIBMStardog-Union
Initial release2013201220002010
Current release7.3.0, May 2020
License infoCommercial or Open Sourcecommercialcommercialcommercialcommercial 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 serviceyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava
Server operating systemshostedhostedLinux infoincluded in applianceLinux
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.yesnono infoImport/export of XML data possible
Secondary indexesyesnoyesyes infosupports real-time indexing in full-text and geospatial
SQL infoSupport of SQLwith Databricks SQLnoyesYes, compatible with all major SQL variants through dedicated BI/SQL Server
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
RESTful HTTP APIJDBC
ODBC
OLE DB
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
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C++
Fortran
Java
Lua
Perl
Python
R
.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions and aggregatesnoyesuser defined functions and aggregates, HTTP Server extensions in Java
Triggersnonoyes infovia event handlers
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud serviceShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationMulti-source replication in HA-Cluster
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency in HA-Cluster
Foreign keys infoReferential integritynonoyes inforelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDoptimistic lockingACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.nonoyes
User concepts infoAccess controlAccess rights based on private key authentication or shared access signaturesUsers with fine-grained authorization conceptAccess rights for users and roles
More information provided by the system vendor
DatabricksMicrosoft Azure Table StorageNetezza infoAlso called PureData System for Analytics by IBMStardog
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksMicrosoft Azure Table StorageNetezza infoAlso called PureData System for Analytics by IBMStardog
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