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DBMS > Brytlyt vs. Databricks vs. GridDB vs. HBase

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

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Editorial information provided by DB-Engines
NameBrytlyt  Xexclude from comparisonDatabricks  Xexclude from comparisonGridDB  Xexclude from comparisonHBase  Xexclude from comparison
DescriptionScalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQLThe 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 modelRelational DBMSDocument store
Relational DBMS
Time Series DBMSWide column store
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.29
Rank#288  Overall
#131  Relational DBMS
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score30.50
Rank#26  Overall
#2  Wide column stores
Websitebrytlyt.iowww.databricks.comgriddb.nethbase.apache.org
Technical documentationdocs.brytlyt.iodocs.databricks.comdocs.griddb.nethbase.apache.org/­book.html
DeveloperBrytlytDatabricksToshiba CorporationApache Software Foundation infoApache top-level project, originally developed by Powerset
Initial release2016201320132008
Current release5.0, August 20235.1, August 20222.3.4, January 2021
License infoCommercial or Open SourcecommercialcommercialOpen 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++ and CUDAC++Java
Server operating systemsLinux
OS X
Windows
hostedLinuxLinux
Unix
Windows infousing Cygwin
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)yesschema-free, schema definition possible
Typing infopredefined data types such as float or dateyesyes 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.yes infospecific XML-type available, but no XML query functionality.yesnono
Secondary indexesyesyesyesno
SQL infoSupport of SQLyeswith Databricks SQLSQL92, SQL-like TQL (Toshiba Query Language)no
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Java API
RESTful HTTP API
Thrift
Supported programming languages.Net
C
C++
Delphi
Java
Perl
Python
Tcl
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 proceduresuser defined functions infoin PL/pgSQLuser defined functions and aggregatesnoyes infoCoprocessors in Java
Triggersyesyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyesSource-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 systemImmediate ConsistencyImmediate ConsistencyImmediate consistency within container, eventual consistency across containersImmediate Consistency or Eventual Consistency
Foreign keys infoReferential integrityyesnono
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 datayesyesyesyes
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.noyesyes
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess 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
BrytlytDatabricksGridDBHBase
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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BrytlytDatabricksGridDBHBase
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