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DBMS > Databricks vs. HEAVY.AI vs. Quasardb

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

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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 comparisonQuasardb  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 hardwareDistributed, high-performance timeseries database
Primary database modelDocument store
Relational DBMS
Relational DBMSTime Series DBMS
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
Score0.14
Rank#332  Overall
#29  Time Series DBMS
Websitewww.databricks.comgithub.com/­heavyai/­heavydb
www.heavy.ai
quasar.ai
Technical documentationdocs.databricks.comdocs.heavy.aidoc.quasar.ai/­master
DeveloperDatabricksHEAVY.AI, Inc.quasardb
Initial release201320162009
Current release5.10, January 20223.14.1, January 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; enterprise edition availablecommercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licenses
Cloud-based only infoOnly available as a cloud serviceyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++ and CUDAC++
Server operating systemshostedLinuxBSD
Linux
OS X
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesschema-free
Typing infopredefined data types such as float or dateyesyes infointeger and binary
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 infowith tags
SQL infoSupport of SQLwith Databricks SQLyesSQL-like query language
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Thrift
Vega
HTTP API
Supported programming languagesPython
R
Scala
All languages supporting JDBC/ODBC/Thrift
Python
.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
Server-side scripts infoStored proceduresuser defined functions and aggregatesnono
Triggersnono
Partitioning methods infoMethods for storing different data on different nodesSharding infoRound robinSharding infoconsistent hashing
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replicationSource-replica replication with selectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsnowith Hadoop integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes infoby using LevelDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes infoTransient mode
User concepts infoAccess controlfine grained access rights according to SQL-standardCryptographically strong user authentication and audit trail
More information provided by the system vendor
DatabricksHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Quasardb
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 2022Quasardb
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