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DBMS > Databend vs. Databricks vs. HEAVY.AI

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

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Editorial information provided by DB-Engines
NameDatabend  Xexclude from comparisonDatabricks  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparison
DescriptionAn open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexityThe 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 hardware
Primary database modelRelational DBMSDocument store
Relational DBMS
Relational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.28
Rank#287  Overall
#131  Relational DBMS
Score84.24
Rank#14  Overall
#2  Document stores
#9  Relational DBMS
Score1.41
Rank#153  Overall
#71  Relational DBMS
Websitegithub.com/­datafuselabs/­databend
www.databend.com
www.databricks.comgithub.com/­heavyai/­heavydb
www.heavy.ai
Technical documentationdocs.databend.comdocs.databricks.comdocs.heavy.ai
DeveloperDatabend LabsDatabricksHEAVY.AI, Inc.
Initial release202120132016
Current release1.0.59, April 20235.10, January 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2; enterprise edition available
Cloud-based only infoOnly available as a cloud servicenoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageRustC++ and CUDA
Server operating systemshosted
Linux
macOS
hostedLinux
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)yes
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.noyesno
Secondary indexesnoyesno
SQL infoSupport of SQLyeswith Databricks SQLyes
APIs and other access methodsCLI Client
JDBC
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Thrift
Vega
Supported programming languagesGo
Java
JavaScript (Node.js)
Python
Rust
Python
R
Scala
All languages supporting JDBC/ODBC/Thrift
Python
Server-side scripts infoStored proceduresnouser defined functions and aggregatesno
Triggersnono
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infoRound robin
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyesMulti-source replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
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 datayesACIDno
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.noyes
User concepts infoAccess controlUsers with fine-grained authorization concept, user rolesfine grained access rights according to SQL-standard
More information provided by the system vendor
DatabendDatabricksHEAVY.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
DatabendDatabricksHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022
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