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DBMS > EventStoreDB vs. GBase vs. Netezza vs. Spark SQL

System Properties Comparison EventStoreDB vs. GBase vs. Netezza vs. Spark SQL

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Editorial information provided by DB-Engines
NameEventStoreDB  Xexclude from comparisonGBase  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionIndustrial-strength, open-source database solution built from the ground up for event sourcing.Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.Data warehouse and analytics appliance part of IBM PureSystemsSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelEvent StoreRelational DBMSRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.10
Rank#179  Overall
#1  Event Stores
Score1.07
Rank#185  Overall
#86  Relational DBMS
Score9.06
Rank#46  Overall
#29  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitewww.eventstore.comwww.gbase.cnwww.ibm.com/­products/­netezzaspark.apache.org/­sql
Technical documentationdevelopers.eventstore.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperEvent Store LimitedGeneral Data Technology Co., Ltd.IBMApache Software Foundation
Initial release2012200420002014
Current release21.2, February 2021GBase 8a, GBase 8s, GBase 8c3.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen SourcecommercialcommercialOpen 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, Java, PythonScala
Server operating systemsLinux
Windows
LinuxLinux infoincluded in applianceLinux
OS X
Windows
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesyesyes
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.yesno
Secondary indexesyesyesno
SQL infoSupport of SQLStandard with numerous extensionsyesSQL-like DML and DDL statements
APIs and other access methodsADO.NET
C API
JDBC
ODBC
JDBC
ODBC
OLE DB
JDBC
ODBC
Supported programming languagesC#C
C++
Fortran
Java
Lua
Perl
Python
R
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsyesno
Triggersyesnono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by range, list and hash) and vertical partitioningShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyesSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Foreign keys infoReferential integrityyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDno
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.no
User concepts infoAccess controlyesUsers with fine-grained authorization conceptno

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More resources
EventStoreDBGBaseNetezza infoAlso called PureData System for Analytics by IBMSpark SQL
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