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DBMS > Databricks vs. Pinecone vs. PostGIS vs. WakandaDB

System Properties Comparison Databricks vs. Pinecone vs. PostGIS vs. WakandaDB

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonPinecone  Xexclude from comparisonPostGIS  Xexclude from comparisonWakandaDB  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 managed, cloud-native vector databaseSpatial extension of PostgreSQLWakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data
Primary database modelDocument store
Relational DBMS
Vector DBMSSpatial DBMSObject oriented DBMS
Secondary database modelsRelational 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
Score3.16
Rank#95  Overall
#2  Vector DBMS
Score22.69
Rank#29  Overall
#1  Spatial DBMS
Score0.03
Rank#364  Overall
#17  Object oriented DBMS
Websitewww.databricks.comwww.pinecone.iopostgis.netwakanda.github.io
Technical documentationdocs.databricks.comdocs.pinecone.io/­docs/­overviewpostgis.net/­documentationwakanda.github.io/­doc
DeveloperDatabricksPinecone Systems, IncWakanda SAS
Initial release2013201920052012
Current release3.4.2, February 20242.7.0 (April 29, 2019), April 2019
License infoCommercial or Open SourcecommercialcommercialOpen Source infoGPL v2.0Open Source infoAGPLv3, extended commercial license available
Cloud-based only infoOnly available as a cloud serviceyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCC++, JavaScript
Server operating systemshostedhostedLinux
OS X
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyes
Typing infopredefined data types such as float or dateString, Number, Booleanyesyes
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.yesnoyesno
Secondary indexesyesyes
SQL infoSupport of SQLwith Databricks SQLnoyesno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
RESTful HTTP APIRESTful HTTP API
Supported programming languagesPython
R
Scala
PythonJavaScript
Server-side scripts infoStored proceduresuser defined functions and aggregatesuser defined functionsyes
Triggersyesyes
Partitioning methods infoMethods for storing different data on different nodesyes infobased on PostgreSQLnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes infobased on PostgreSQLnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID
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.nononono
User concepts infoAccess controlyes infobased on PostgreSQLyes
More information provided by the system vendor
DatabricksPineconePostGISWakandaDB
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksPineconePostGISWakandaDB
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