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DBMS > Databricks vs. HEAVY.AI vs. Qdrant

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

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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 comparisonQdrant  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 hardwareA high-performance vector database with neural network or semantic-based matching
Primary database modelDocument store
Relational DBMS
Relational DBMSVector DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score90.03
Rank#13  Overall
#2  Document stores
#9  Relational DBMS
Score1.25
Rank#161  Overall
#72  Relational DBMS
Score1.94
Rank#118  Overall
#8  Vector DBMS
Websitewww.databricks.comgithub.com/­heavyai/­heavydb
www.heavy.ai
github.com/­qdrant/­qdrant
qdrant.tech
Technical documentationdocs.databricks.comdocs.heavy.aiqdrant.tech/­documentation
DeveloperDatabricksHEAVY.AI, Inc.Qdrant
Initial release201320162021
Current release5.10, January 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; enterprise edition availableOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++ and CUDARust
Server operating systemshostedLinuxDocker
Linux
macOS
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesschema-free
Typing infopredefined data types such as float or dateyesNumbers, Strings, Geo, Boolean
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 infoKeywords, numberic ranges, geo, full-text
SQL infoSupport of SQLwith Databricks SQLyesno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Thrift
Vega
gRPC
OpenAPI 3.0
RESTful HTTP/JSON API infoOpenAPI 3.0
Supported programming languagesPython
R
Scala
All languages supporting JDBC/ODBC/Thrift
Python
.Net
Go
Java
JavaScript (Node.js)
Python
Rust
Server-side scripts infoStored proceduresuser defined functions and aggregatesno
Triggersno
Partitioning methods infoMethods for storing different data on different nodesSharding infoRound robinSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replicationCollection-level replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency, tunable consistency
Foreign keys infoReferential integrityno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDno
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.noyesyes
User concepts infoAccess controlfine grained access rights according to SQL-standardKey-based authentication
More information provided by the system vendor
DatabricksHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Qdrant
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Qdrant
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