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DBMS > Databend vs. HEAVY.AI vs. Spark SQL

System Properties Comparison Databend vs. HEAVY.AI vs. Spark SQL

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Editorial information provided by DB-Engines
NameDatabend  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonSpark SQL  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 complexityA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.34
Rank#283  Overall
#130  Relational DBMS
Score1.64
Rank#145  Overall
#67  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitegithub.com/­datafuselabs/­databend
www.databend.com
github.com/­heavyai/­heavydb
www.heavy.ai
spark.apache.org/­sql
Technical documentationdocs.databend.comdocs.heavy.aispark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperDatabend LabsHEAVY.AI, Inc.Apache Software Foundation
Initial release202120162014
Current release1.0.59, April 20235.10, January 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2; enterprise edition availableOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageRustC++ and CUDAScala
Server operating systemshosted
Linux
macOS
LinuxLinux
OS X
Windows
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesyesyes
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.nonono
Secondary indexesnonono
SQL infoSupport of SQLyesyesSQL-like DML and DDL statements
APIs and other access methodsCLI Client
JDBC
RESTful HTTP API
JDBC
ODBC
Thrift
Vega
JDBC
ODBC
Supported programming languagesGo
Java
JavaScript (Node.js)
Python
Rust
All languages supporting JDBC/ODBC/Thrift
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresnonono
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infoRound robinyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesnono
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.yesno
User concepts infoAccess controlUsers with fine-grained authorization concept, user rolesfine grained access rights according to SQL-standardno

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More resources
DatabendHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Spark SQL
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