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DBMS > Databricks vs. Datomic vs. HEAVY.AI vs. Kinetica vs. Solr

System Properties Comparison Databricks vs. Datomic vs. HEAVY.AI vs. Kinetica vs. Solr

Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonDatomic  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonKinetica  Xexclude from comparisonSolr  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.Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareFully vectorized database across both GPUs and CPUsA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelDocument store
Relational DBMS
Relational DBMSRelational DBMSRelational DBMSSearch engine
Secondary database modelsSpatial DBMSSpatial DBMS
Time Series DBMS
Spatial 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
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Websitewww.databricks.comwww.datomic.comgithub.com/­heavyai/­heavydb
www.heavy.ai
www.kinetica.comsolr.apache.org
Technical documentationdocs.databricks.comdocs.datomic.comdocs.heavy.aidocs.kinetica.comsolr.apache.org/­resources.html
DeveloperDatabricksCognitectHEAVY.AI, Inc.KineticaApache Software Foundation
Initial release20132012201620122006
Current release1.0.6735, June 20235.10, January 20227.1, August 20219.6.0, April 2024
License infoCommercial or Open Sourcecommercialcommercial infolimited edition freeOpen Source infoApache Version 2; enterprise edition availablecommercialOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, ClojureC++ and CUDAC, C++Java
Server operating systemshostedAll OS with a Java VMLinuxLinuxAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyesyesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyesyes infosupports customizable data types and automatic typing
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.yesnononoyes
Secondary indexesyesyesnoyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLwith Databricks SQLnoyesSQL-like DML and DDL statementsSolr Parallel SQL Interface
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
RESTful HTTP APIJDBC
ODBC
Thrift
Vega
JDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP/JSON API
Supported programming languagesPython
R
Scala
Clojure
Java
All languages supporting JDBC/ODBC/Thrift
Python
C++
Java
JavaScript (Node.js)
Python
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes infoTransaction Functionsnouser defined functionsJava plugins
TriggersBy using transaction functionsnoyes infotriggers when inserted values for one or more columns fall within a specified rangeyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersSharding infoRound robinShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesnone infoBut extensive use of caching in the application peersMulti-source replicationSource-replica replicationyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationEventual Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnonooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes inforecommended only for testing and developmentyesyes infoGPU vRAM or System RAMyes
User concepts infoAccess controlnofine grained access rights according to SQL-standardAccess rights for users and roles on table levelyes
More information provided by the system vendor
DatabricksDatomicHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022KineticaSolr
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksDatomicHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022KineticaSolr
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