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DBMS > Databricks vs. Solr vs. WakandaDB

System Properties Comparison Databricks vs. Solr vs. WakandaDB

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonSolr  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 widely used distributed, scalable search engine based on Apache LuceneWakandaDB 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
Search engineObject oriented DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score60.74
Rank#19  Overall
#3  Document stores
#13  Relational DBMS
Score48.26
Rank#24  Overall
#3  Search engines
Score0.01
Rank#374  Overall
#21  Object oriented DBMS
Websitedatabricks.comlucene.apache.org/­solrwakanda.github.io
Technical documentationdatabricks.com/­documentationlucene.apache.org/­solr/­resources.htmldoc.wakanda.org/­home2.en.html#/­Datastore/­Introduction.200-588937.en.html
DeveloperDatabricksApache Software FoundationWakanda SAS
Initial release201320062012
Current release9.0.0, May 20222.7.0 (April 29, 2019), April 2019
License infoCommercial or Open SourceOpen SourceOpen Source infoApache Version 2Open Source infoAGPLv3, extended commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++, JavaScript
Server operating systemshostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Linux
OS X
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yes infoDynamic Fields enables on-the-fly addition of new fieldsyes
Typing infopredefined data types such as float or dateyes infosupports customizable data types and automatic typingyes
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.yesyesno
Secondary indexesyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLwith Databricks SQLSolr Parallel SQL Interfaceno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP/JSON API
RESTful HTTP API
Supported programming languagesPython
R
Scala
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
JavaScript
Server-side scripts infoStored proceduresuser defined functions and aggregatesJava pluginsyes
Triggersyes infoUser configurable commands triggered on index changesyes
Partitioning methods infoMethods for storing different data on different nodesShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integrityno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDoptimistic lockingACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlyesyes

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More resources
DatabricksSolrWakandaDB
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