DBMS > Databricks vs. MonetDB vs. SwayDB
System Properties Comparison Databricks vs. MonetDB vs. SwayDB
Please select another system to include it in the comparison.
|Editorial information provided by DB-Engines
|Databricks Xexclude from comparison
|MonetDB Xexclude from comparison
|SwayDB Xexclude from comparison
|The 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 relational database management system that stores data in columns
|An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage
|Primary database model
|Secondary database models
|Dec2023 (11.49), December 2023
|License Commercial or Open Source
|Open Source Mozilla Public License 2.0
|Open Source GNU Affero GPL V3.0
|Cloud-based only Only available as a cloud service
|DBaaS offerings (sponsored links) Database as a Service
Providers of DBaaS offerings, please contact us to be listed.
|Server operating systems
|Flexible Schema (defined schema, partial schema, schema free)
|Typing predefined data types such as float or date
|XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.
|SQL Support of SQL
|with Databricks SQL
|yes SQL 2003 with some extensions
|APIs and other access methods
RESTful HTTP API
native C library MAPI library (MonetDB application programming interface)
|Supported programming languages
|Server-side scripts Stored procedures
|user defined functions and aggregates
|yes, in SQL, C, R
|Partitioning methods Methods for storing different data on different nodes
|Sharding via remote tables
|Replication methods Methods for redundantly storing data on multiple nodes
|none Source-replica replication available in experimental status
|MapReduce Offers an API for user-defined Map/Reduce methods
|Consistency concepts Methods to ensure consistency in a distributed system
|Foreign keys Referential integrity
|Transaction concepts Support to ensure data integrity after non-atomic manipulations of data
|Atomic execution of operations
|Concurrency Support for concurrent manipulation of data
|Durability Support for making data persistent
|In-memory capabilities Is there an option to define some or all structures to be held in-memory only.
|User concepts Access control
|fine grained access rights according to SQL-standard
|More information provided by the system vendor
|Supported database models : In addition to the Document store and Relational DBMS...
We invite representatives of system vendors to contact us for updating and extending the system information,
Related products and services
We invite representatives of vendors of related products to contact us for presenting information about their offerings here.
|DB-Engines blog posts
|Recent citations in the news
Grammarly Helps Databricks Elevate Customer Trust and Efficiency With Its AI Writing Assistance
Databricks Doubles Down on Data to Bring AI to Fortune 500
Grammarly Drives Databricks' Productivity Surge, $1.4M Annual Savings with AI Writing Tools
Grammarly Delivers a 1994% ROI for Databricks According to Nucleus Research ROI Case Study
provided by Google News
In 2024 the MonetDB Foundation was established for the preservation, maintenance and further development of the ...
MonetDB Secures Investment From (and Partners With) ServiceNow
How MonetDB Exploits Modern CPU Performance | by Dwi Prasetyo Adi Nugroho
PostgreSQL, MonetDB, and Too-Big-for-Memory Data in R - Part I - DataScienceCentral.com
Monet DB The Column-Store Pioneer - open source for you
provided by Google News
Share this page