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DBMS > Bangdb vs. Databricks vs. GridDB vs. HBase

System Properties Comparison Bangdb vs. Databricks vs. GridDB vs. HBase

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Editorial information provided by DB-Engines
NameBangdb  Xexclude from comparisonDatabricks  Xexclude from comparisonGridDB  Xexclude from comparisonHBase  Xexclude from comparison
DescriptionConverged and high performance database for device data, events, time series, document and graphThe 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 DataWide-column store based on Apache Hadoop and on concepts of BigTable
Primary database modelDocument store
Graph DBMS
Time Series DBMS
Document store
Relational DBMS
Time Series DBMSWide column store
Secondary database modelsSpatial DBMSKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.16
Rank#338  Overall
#47  Document stores
#32  Graph DBMS
#31  Time Series DBMS
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score27.97
Rank#26  Overall
#2  Wide column stores
Websitebangdb.comwww.databricks.comgriddb.nethbase.apache.org
Technical documentationdocs.bangdb.comdocs.databricks.comdocs.griddb.nethbase.apache.org/­book.html
DeveloperSachin Sinha, BangDBDatabricksToshiba CorporationApache Software Foundation infoApache top-level project, originally developed by Powerset
Initial release2012201320132008
Current releaseBangDB 2.0, October 20215.1, August 20222.3.4, January 2021
License infoCommercial or Open SourceOpen Source infoBSD 3commercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache version 2
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++C++Java
Server operating systemsLinuxhostedLinuxLinux
Unix
Windows infousing Cygwin
Data schemeschema-freeFlexible Schema (defined schema, partial schema, schema free)yesschema-free, schema definition possible
Typing infopredefined data types such as float or dateyes: string, long, double, int, geospatial, stream, eventsyes infonumerical, string, blob, geometry, boolean, timestampoptions to bring your own types, AVRO
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 indexesyes infosecondary, composite, nested, reverse, geospatialyesyesno
SQL infoSupport of SQLSQL like support with command line toolwith Databricks SQLSQL92, SQL-like TQL (Toshiba Query Language)no
APIs and other access methodsProprietary protocol
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Java API
RESTful HTTP API
Thrift
Supported programming languagesC
C#
C++
Java
Python
Python
R
Scala
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C
C#
C++
Groovy
Java
PHP
Python
Scala
Server-side scripts infoStored proceduresnouser defined functions and aggregatesnoyes infoCoprocessors in Java
Triggersyes, Notifications (with Streaming only)yesyes
Partitioning methods infoMethods for storing different data on different nodesSharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithmShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor, Knob for CAP (enterprise version only)yesSource-replica replicationMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsyes
Consistency concepts infoMethods to ensure consistency in a distributed systemTunable consistency, set CAP knob accordinglyImmediate ConsistencyImmediate consistency within container, eventual consistency across containersImmediate Consistency or Eventual Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID at container levelSingle row ACID (across millions of columns)
Concurrency infoSupport for concurrent manipulation of datayes, optimistic concurrency controlyesyesyes
Durability infoSupport for making data persistentyes, implements WAL (Write ahead log) as wellyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes, run db with in-memory only modenoyesyes
User concepts infoAccess controlyes (enterprise version only)Access rights for users can be defined per databaseAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC
More information provided by the system vendor
BangdbDatabricksGridDBHBase
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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BangdbDatabricksGridDBHBase
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