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DBMS > Hazelcast vs. Netezza vs. Spark SQL vs. STSdb

System Properties Comparison Hazelcast vs. Netezza vs. Spark SQL vs. STSdb

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Editorial information provided by DB-Engines
NameHazelcast  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonSpark SQL  Xexclude from comparisonSTSdb  Xexclude from comparison
DescriptionA widely adopted in-memory data gridData warehouse and analytics appliance part of IBM PureSystemsSpark SQL is a component on top of 'Spark Core' for structured data processingKey-Value Store with special method for indexing infooptimized for high performance using a special indexing method
Primary database modelKey-value storeRelational DBMSRelational DBMSKey-value store
Secondary database modelsDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.66
Rank#54  Overall
#5  Key-value stores
Score11.20
Rank#46  Overall
#29  Relational DBMS
Score19.56
Rank#34  Overall
#21  Relational DBMS
Score0.13
Rank#344  Overall
#51  Key-value stores
Websitehazelcast.comwww.ibm.com/­products/­netezzaspark.apache.org/­sqlgithub.com/­STSSoft/­STSdb4
Technical documentationhazelcast.org/­imdg/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperHazelcastIBMApache Software FoundationSTS Soft SC
Initial release2008200020142011
Current release5.3.6, November 20233.5.0 ( 2.13), September 20234.0.8, September 2015
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses availablecommercialOpen Source infoApache 2.0Open Source infoGPLv2, commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScalaC#
Server operating systemsAll OS with a Java VMLinux infoincluded in applianceLinux
OS X
Windows
Windows
Data schemeschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes infoprimitive types and user defined types (classes)
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 infothe object must implement a serialization strategyno
Secondary indexesyesyesnono
SQL infoSupport of SQLSQL-like query languageyesSQL-like DML and DDL statementsno
APIs and other access methodsJCache
JPA
Memcached protocol
RESTful HTTP API
JDBC
ODBC
OLE DB
JDBC
ODBC
.NET Client API
Supported programming languages.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C
C++
Fortran
Java
Lua
Perl
Python
R
Java
Python
R
Scala
C#
Java
Server-side scripts infoStored proceduresyes infoEvent Listeners, Executor Servicesyesnono
Triggersyes infoEventsnonono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoReplicated MapSource-replica replicationnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus Algorithm
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataone or two-phase-commit; repeatable reads; read commitedACIDnono
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 controlRole-based access controlUsers with fine-grained authorization conceptnono

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More resources
HazelcastNetezza infoAlso called PureData System for Analytics by IBMSpark SQLSTSdb
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