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DBMS > BigObject vs. EsgynDB vs. OrigoDB vs. Spark SQL

System Properties Comparison BigObject vs. EsgynDB vs. OrigoDB vs. Spark SQL

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Editorial information provided by DB-Engines
NameBigObject  Xexclude from comparisonEsgynDB  Xexclude from comparisonOrigoDB  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionAnalytic DBMS for real-time computations and queriesEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionA fully ACID in-memory object graph databaseSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMS infoa hierachical model (tree) can be imposedRelational DBMSDocument store
Object oriented DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.19
Rank#329  Overall
#146  Relational DBMS
Score0.25
Rank#312  Overall
#138  Relational DBMS
Score0.06
Rank#380  Overall
#50  Document stores
#19  Object oriented DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitebigobject.iowww.esgyn.cnorigodb.comspark.apache.org/­sql
Technical documentationdocs.bigobject.ioorigodb.com/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperBigObject, Inc.EsgynRobert Friberg et alApache Software Foundation
Initial release201520152009 infounder the name LiveDB2014
Current release3.5.0 ( 2.13), September 2023
License infoCommercial or Open Sourcecommercial infofree community edition availablecommercialOpen SourceOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++, JavaC#Scala
Server operating systemsLinux infodistributed as a docker-image
OS X infodistributed as a docker-image (boot2docker)
Windows infodistributed as a docker-image (boot2docker)
LinuxLinux
Windows
Linux
OS X
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesUser defined using .NET types and collectionsyes
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.nonono infocan be achieved using .NETno
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsyesnoSQL-like DML and DDL statements
APIs and other access methodsfluentd
ODBC
RESTful HTTP API
ADO.NET
JDBC
ODBC
.NET Client API
HTTP API
LINQ
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBC/ADO.Net.NetJava
Python
R
Scala
Server-side scripts infoStored proceduresLuaJava Stored Proceduresyesno
Triggersnonoyes infoDomain Eventsno
Partitioning methods infoMethods for storing different data on different nodesnoneShardinghorizontal partitioning infoclient side managed; servers are not synchronizedyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replication between multi datacentersSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate Consistency
Foreign keys infoReferential integrityyes infoautomatically between fact table and dimension tablesyesdepending on modelno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayes infoRead/write lock on objects (tables, trees)yesyesyes
Durability infoSupport for making data persistentyesyesyes infoWrite ahead logyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesno
User concepts infoAccess controlnofine grained access rights according to SQL-standardRole based authorizationno

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More resources
BigObjectEsgynDBOrigoDBSpark SQL
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