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DBMS > Apache Druid vs. Databricks vs. GridDB vs. Realm

System Properties Comparison Apache Druid vs. Databricks vs. GridDB vs. Realm

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonDatabricks  Xexclude from comparisonGridDB  Xexclude from comparisonRealm  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataThe 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.Scalable in-memory time series database optimized for IoT and Big DataA DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data
Primary database modelRelational DBMS
Time Series DBMS
Document store
Relational DBMS
Time Series DBMSDocument store
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.25
Rank#90  Overall
#47  Relational DBMS
#7  Time Series DBMS
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score7.41
Rank#52  Overall
#8  Document stores
Websitedruid.apache.orgwww.databricks.comgriddb.netrealm.io
Technical documentationdruid.apache.org/­docs/­latest/­designdocs.databricks.comdocs.griddb.netrealm.io/­docs
DeveloperApache Software Foundation and contributorsDatabricksToshiba CorporationRealm, acquired by MongoDB in May 2019
Initial release2012201320132014
Current release29.0.1, April 20245.1, August 2022
License infoCommercial or Open SourceOpen Source infoApache license v2commercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++
Server operating systemsLinux
OS X
Unix
hostedLinuxAndroid
Backend: server-less
iOS
Windows
Data schemeyes infoschema-less columns are supportedFlexible Schema (defined schema, partial schema, schema free)yesyes
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampyes
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.noyesnono
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL for queryingwith Databricks SQLSQL92, SQL-like TQL (Toshiba Query Language)no
APIs and other access methodsJDBC
RESTful HTTP/JSON API
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
Python
R
Scala
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
Java infowith Android only
Objective-C
React Native
Swift
Server-side scripts infoStored proceduresnouser defined functions and aggregatesnono inforuns within the applications so server-side scripts are unnecessary
Triggersnoyesyes infoChange Listeners
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesyesSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate consistency within container, eventual consistency across containersImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID at container levelACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyes infoIn-Memory realm
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemAccess rights for users can be defined per databaseyes
More information provided by the system vendor
Apache DruidDatabricksGridDBRealm
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
» more
GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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More resources
Apache DruidDatabricksGridDBRealm
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