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DBMS > Databricks vs. Interbase vs. LeanXcale vs. Netezza

System Properties Comparison Databricks vs. Interbase vs. LeanXcale vs. Netezza

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonInterbase  Xexclude from comparisonLeanXcale  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  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.Light-weight proven RDBMS infooriginally from BorlandA highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilitiesData warehouse and analytics appliance part of IBM PureSystems
Primary database modelDocument store
Relational DBMS
Relational DBMSKey-value store
Relational DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score4.61
Rank#72  Overall
#39  Relational DBMS
Score0.29
Rank#291  Overall
#41  Key-value stores
#132  Relational DBMS
Score9.06
Rank#46  Overall
#29  Relational DBMS
Websitewww.databricks.comwww.embarcadero.com/­products/­interbasewww.leanxcale.comwww.ibm.com/­products/­netezza
Technical documentationdocs.databricks.comdocs.embarcadero.com/­products/­interbase
DeveloperDatabricksEmbarcaderoLeanXcaleIBM
Initial release2013198420152000
Current releaseInterBase 2020, December 2019
License infoCommercial or Open Sourcecommercialcommercialcommercialcommercial
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC
Server operating systemshostedAndroid
iOS
Linux
OS X
Windows
Linux infoincluded in appliance
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyesyes
Typing infopredefined data types such as float or dateyesyes
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 infoexport as XML data possible
Secondary indexesyesyesyes
SQL infoSupport of SQLwith Databricks SQLyesyes infothrough Apache Derbyyes
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
ODBC
JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector
JDBC
ODBC
OLE DB
Supported programming languagesPython
R
Scala
.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby
C
Java
Scala
C
C++
Fortran
Java
Lua
Perl
Python
R
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes infoInterbase procedure and trigger languageyes
Triggersyesno
Partitioning methods infoMethods for storing different data on different nodesnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesInterbase Change ViewsSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyes infoMultiversion concurreny controlyesyes
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 controlfine grained access rights according to SQL-standardUsers with fine-grained authorization concept
More information provided by the system vendor
DatabricksInterbaseLeanXcaleNetezza infoAlso called PureData System for Analytics by IBM
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksInterbaseLeanXcaleNetezza infoAlso called PureData System for Analytics by IBM
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