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DBMS > Databricks vs. HEAVY.AI vs. PouchDB

System Properties Comparison Databricks vs. HEAVY.AI vs. PouchDB

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonPouchDB  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 high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareJavaScript DBMS with an API inspired by CouchDB
Primary database modelDocument store
Relational DBMS
Relational DBMSDocument store
Secondary database modelsSpatial 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
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score2.28
Rank#115  Overall
#21  Document stores
Websitewww.databricks.comgithub.com/­heavyai/­heavydb
www.heavy.ai
pouchdb.com
Technical documentationdocs.databricks.comdocs.heavy.aipouchdb.com/­guides
DeveloperDatabricksHEAVY.AI, Inc.Apache Software Foundation
Initial release201320162012
Current release5.10, January 20227.1.1, June 2019
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; enterprise edition availableOpen Source
Cloud-based only infoOnly available as a cloud serviceyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++ and CUDAJavaScript
Server operating systemshostedLinuxserver-less, requires a JavaScript environment (browser, Node.js)
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesschema-free
Typing infopredefined data types such as float or dateyesno
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.yesnono
Secondary indexesyesnoyes infovia views
SQL infoSupport of SQLwith Databricks SQLyesno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Thrift
Vega
HTTP REST infoonly for PouchDB Server
JavaScript API
Supported programming languagesPython
R
Scala
All languages supporting JDBC/ODBC/Thrift
Python
JavaScript
Server-side scripts infoStored proceduresuser defined functions and aggregatesnoView functions in JavaScript
Triggersnoyes
Partitioning methods infoMethods for storing different data on different nodesSharding infoRound robinSharding infowith a proxy-based framework, named couchdb-lounge
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replicationMulti-source replication infoalso with CouchDB databases
Source-replica replication infoalso with CouchDB databases
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyesyes infoby using IndexedDB, WebSQL or LevelDB as backend
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-standardno
More information provided by the system vendor
DatabricksHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022PouchDB
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022PouchDB
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