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DBMS > Brytlyt vs. HBase vs. RavenDB vs. Spark SQL

System Properties Comparison Brytlyt vs. HBase vs. RavenDB vs. Spark SQL

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Editorial information provided by DB-Engines
NameBrytlyt  Xexclude from comparisonHBase  Xexclude from comparisonRavenDB  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionScalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQLWide-column store based on Apache Hadoop and on concepts of BigTableOpen Source Operational and Transactional Enterprise NoSQL Document DatabaseSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSWide column storeDocument storeRelational DBMS
Secondary database modelsGraph DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.38
Rank#276  Overall
#127  Relational DBMS
Score27.97
Rank#26  Overall
#2  Wide column stores
Score2.84
Rank#101  Overall
#18  Document stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitebrytlyt.iohbase.apache.orgravendb.netspark.apache.org/­sql
Technical documentationdocs.brytlyt.iohbase.apache.org/­book.htmlravendb.net/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperBrytlytApache Software Foundation infoApache top-level project, originally developed by PowersetHibernating RhinosApache Software Foundation
Initial release2016200820102014
Current release5.0, August 20232.3.4, January 20215.4, July 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache version 2Open Source infoAGPL version 3, commercial license availableOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++ and CUDAJavaC#Scala
Server operating systemsLinux
OS X
Windows
Linux
Unix
Windows infousing Cygwin
Linux
macOS
Raspberry Pi
Windows
Linux
OS X
Windows
Data schemeyesschema-free, schema definition possibleschema-freeyes
Typing infopredefined data types such as float or dateyesoptions to bring your own types, AVROnoyes
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.nono
Secondary indexesyesnoyesno
SQL infoSupport of SQLyesnoSQL-like query language (RQL)SQL-like DML and DDL statements
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Java API
RESTful HTTP API
Thrift
.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API
JDBC
ODBC
Supported programming languages.Net
C
C++
Delphi
Java
Perl
Python
Tcl
C
C#
C++
Groovy
Java
PHP
Python
Scala
.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions infoin PL/pgSQLyes infoCoprocessors in Javayesno
Triggersyesyesyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-source replication
Source-replica replication
Multi-source replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual ConsistencyDefault ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDSingle row ACID (across millions of columns)ACID, Cluster-wide transaction availableno
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.yesno
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACAuthorization levels configured per client per databaseno

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More resources
BrytlytHBaseRavenDBSpark SQL
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