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DBMS > Badger vs. Databricks vs. Netezza vs. SpaceTime

System Properties Comparison Badger vs. Databricks vs. Netezza vs. SpaceTime

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Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonDatabricks  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonSpaceTime  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.The 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.Data warehouse and analytics appliance part of IBM PureSystemsSpaceTime is a spatio-temporal DBMS with a focus on performance.
Primary database modelKey-value storeDocument store
Relational DBMS
Relational DBMSSpatial DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.22
Rank#320  Overall
#47  Key-value stores
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score8.59
Rank#45  Overall
#29  Relational DBMS
Score0.03
Rank#392  Overall
#8  Spatial DBMS
Websitegithub.com/­dgraph-io/­badgerwww.databricks.comwww.ibm.com/­products/­netezzawww.mireo.com/­spacetime
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgerdocs.databricks.com
DeveloperDGraph LabsDatabricksIBMMireo
Initial release2017201320002020
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoC++
Server operating systemsBSD
Linux
OS X
Solaris
Windows
hostedLinux infoincluded in applianceLinux
Data schemeschema-freeFlexible Schema (defined schema, partial schema, schema free)yesyes
Typing infopredefined data types such as float or datenoyesyes
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.noyesno
Secondary indexesnoyesyesno
SQL infoSupport of SQLnowith Databricks SQLyesA subset of ANSI SQL is implemented
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
OLE DB
RESTful HTTP API
Supported programming languagesGoPython
R
Scala
C
C++
Fortran
Java
Lua
Perl
Python
R
C#
C++
Python
Server-side scripts infoStored proceduresnouser defined functions and aggregatesyesno
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesnoneShardingFixed-grid hypercubes
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyesSource-replica replicationReal-time block device replication (DRBD)
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDno
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.nonono
User concepts infoAccess controlnoUsers with fine-grained authorization conceptyes
More information provided by the system vendor
BadgerDatabricksNetezza infoAlso called PureData System for Analytics by IBMSpaceTime
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
BadgerDatabricksNetezza infoAlso called PureData System for Analytics by IBMSpaceTime
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