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DBMS > EventStoreDB vs. Spark SQL vs. VelocityDB vs. Yanza

System Properties Comparison EventStoreDB vs. Spark SQL vs. VelocityDB vs. Yanza

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Editorial information provided by DB-Engines
NameEventStoreDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonVelocityDB  Xexclude from comparisonYanza  Xexclude from comparison
Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionIndustrial-strength, open-source database solution built from the ground up for event sourcing.Spark SQL is a component on top of 'Spark Core' for structured data processingA .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)Time Series DBMS for IoT Applications
Primary database modelEvent StoreRelational DBMSGraph DBMS
Object oriented DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.19
Rank#173  Overall
#1  Event Stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.11
Rank#354  Overall
#37  Graph DBMS
#15  Object oriented DBMS
Websitewww.eventstore.comspark.apache.org/­sqlvelocitydb.comyanza.com
Technical documentationdevelopers.eventstore.comspark.apache.org/­docs/­latest/­sql-programming-guide.htmlvelocitydb.com/­UserGuide
DeveloperEvent Store LimitedApache Software FoundationVelocityDB IncYanza
Initial release2012201420112015
Current release21.2, February 20213.5.0 ( 2.13), September 20237.x
License infoCommercial or Open SourceOpen SourceOpen Source infoApache 2.0commercialcommercial infofree version available
Cloud-based only infoOnly available as a cloud servicenononono infobut mainly used as a service provided by Yanza
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaC#
Server operating systemsLinux
Windows
Linux
OS X
Windows
Any that supports .NETWindows
Data schemeyesyesschema-free
Typing infopredefined data types such as float or dateyesyesno
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
Secondary indexesnoyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsnono
APIs and other access methodsJDBC
ODBC
.NetHTTP API
Supported programming languagesJava
Python
R
Scala
.Netany language that supports HTTP calls
Server-side scripts infoStored proceduresnonono
TriggersnoCallbacks are triggered when data changesyes infoTimer and event based
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark CoreShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlnoBased on Windows Authenticationno

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More resources
EventStoreDBSpark SQLVelocityDBYanza
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