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DBMS > Databricks vs. Firebolt vs. HEAVY.AI

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

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonFirebolt  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  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.Highly scalable cloud data warehouse and analytics product infoForked from ClickhouseA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware
Primary database modelDocument store
Relational DBMS
Relational DBMSRelational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score1.73
Rank#140  Overall
#63  Relational DBMS
Score1.64
Rank#145  Overall
#67  Relational DBMS
Websitewww.databricks.comwww.firebolt.iogithub.com/­heavyai/­heavydb
www.heavy.ai
Technical documentationdocs.databricks.comdocs.firebolt.iodocs.heavy.ai
DeveloperDatabricksFirebolt Analytics Inc.HEAVY.AI, Inc.
Initial release201320202016
Current release5.10, January 2022
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache Version 2; enterprise edition available
Cloud-based only infoOnly available as a cloud serviceyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++ and CUDA
Server operating systemshostedhostedLinux
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyes
Typing infopredefined data types such as float or dateyesyes
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.yesno
Secondary indexesyesyesno
SQL infoSupport of SQLwith Databricks SQLyesyes
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
.Net
ODBC
RESTful HTTP API
JDBC
ODBC
Thrift
Vega
Supported programming languagesPython
R
Scala
Go
JavaScript (Node.js)
Python
All languages supporting JDBC/ODBC/Thrift
Python
Server-side scripts infoStored proceduresuser defined functions and aggregatesnono
Triggersnono
Partitioning methods infoMethods for storing different data on different nodesSharding infoRound robin
Replication methods infoMethods for redundantly storing data on multiple nodesyesdepending on storage layerMulti-source replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDno
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlfine grained access rights according to SQL-standard
More information provided by the system vendor
DatabricksFireboltHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksFireboltHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022
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