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DBMS > Databricks vs. GridGain vs. Qdrant

System Properties Comparison Databricks vs. GridGain vs. Qdrant

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonGridGain  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.GridGain is an in-memory computing platform, built on Apache IgniteA high-performance vector database with neural network or semantic-based matching
Primary database modelDocument store
Relational DBMS
Columnar
Key-value store
Object oriented DBMS
Relational DBMS
Vector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score102.66
Rank#12  Overall
#2  Document stores
#8  Relational DBMS
Score1.33
Rank#154  Overall
#1  Columnar
#26  Key-value stores
#2  Object oriented DBMS
#70  Relational DBMS
Score2.28
Rank#106  Overall
#8  Vector DBMS
Websitewww.databricks.comwww.gridgain.comgithub.com/­qdrant/­qdrant
qdrant.tech
Technical documentationdocs.databricks.comwww.gridgain.com/­docs/­index.htmlqdrant.tech/­documentation
DeveloperDatabricksGridGain Systems, Inc.Qdrant
Initial release201320072021
Current releaseGridGain 8.5.1
License infoCommercial or Open Sourcecommercialcommercial, open sourceOpen 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 languageJava, C++, .Net, Python, REST, SQLRust
Server operating systemshostedLinux
OS X
Solaris
Windows
z/OS
Docker
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.yesyesno
Secondary indexesyesyesyes infoKeywords, numberic ranges, geo, full-text
SQL infoSupport of SQLwith Databricks SQLANSI-99 for query and DML statements, subset of DDLno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
gRPC
OpenAPI 3.0
RESTful HTTP/JSON API infoOpenAPI 3.0
Supported programming languagesPython
R
Scala
C#
C++
Java
PHP
Python
Ruby
Scala
.Net
Go
Java
JavaScript (Node.js)
Python
Rust
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes (compute grid and cache interceptors can be used instead)
Triggersyes (cache interceptors and events)
Partitioning methods infoMethods for storing different data on different nodesShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes (replicated cache)Collection-level replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes (compute grid and hadoop accelerator)no
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 dataACIDACID
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 controlRole-based access control
Security Hooks for custom implementations
Key-based authentication

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DatabricksGridGainQdrant
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