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DBMS > GigaSpaces vs. MarkLogic vs. Spark SQL vs. Teradata Aster

System Properties Comparison GigaSpaces vs. MarkLogic vs. Spark SQL vs. Teradata Aster

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Editorial information provided by DB-Engines
NameGigaSpaces  Xexclude from comparisonMarkLogic  Xexclude from comparisonSpark SQL  Xexclude from comparisonTeradata Aster  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionHigh performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented TransactionsOperational and transactional Enterprise NoSQL databaseSpark SQL is a component on top of 'Spark Core' for structured data processingPlatform for big data analytics on multistructured data sources and types
Primary database modelDocument store
Object oriented DBMS infoValues are user defined objects
Document store
Native XML DBMS
RDF store infoas of version 7
Search engine
Relational DBMSRelational DBMS
Secondary database modelsGraph DBMS
Search engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.02
Rank#192  Overall
#32  Document stores
#6  Object oriented DBMS
Score6.50
Rank#56  Overall
#10  Document stores
#1  Native XML DBMS
#1  RDF stores
#5  Search engines
Score19.15
Rank#33  Overall
#20  Relational DBMS
Websitewww.gigaspaces.comwww.marklogic.comspark.apache.org/­sql
Technical documentationdocs.gigaspaces.com/­latest/­landing.htmldocs.marklogic.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperGigaspaces TechnologiesMarkLogic Corp.Apache Software FoundationTeradata
Initial release2000200120142005
Current release15.5, September 202011.0, December 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2; Commercial licenses availablecommercial inforestricted free version is availableOpen Source infoApache 2.0commercial
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 languageJava, C++, .NetC++Scala
Server operating systemsLinux
macOS
Solaris
Windows
Linux
OS X
Windows
Linux
OS X
Windows
Linux
Data schemeschema-freeschema-free infoSchema can be enforcedyesFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
Typing infopredefined data types such as float or dateyesyesyesyes
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.no infoXML can be used for describing objects metadatayesnoyes infoin Aster File Store
Secondary indexesyesyesnoyes
SQL infoSupport of SQLSQL-99 for query and DML statementsyes infoSQL92SQL-like DML and DDL statementsyes
APIs and other access methodsGigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
JDBC
ODBC
ADO.NET
JDBC
ODBC
OLE DB
Supported programming languages.Net
C++
Java
Python
Scala
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Java
Python
R
Scala
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresyesyes infovia XQuery or JavaScriptnoR packages
Triggersyes, event driven architectureyesnono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark CoreSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
yesnoneyes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoMap-Reduce pattern can be built with XAP task executorsyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency infoConsistency level configurable: ALL, QUORUM, ANYImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infocan act as a resource manager in an XA/JTA transactionnoACID
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.yesyes, with Range Indexesnono
User concepts infoAccess controlRole-based access controlRole-based access control at the document and subdocument levelsnofine grained access rights according to SQL-standard

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GigaSpacesMarkLogicSpark SQLTeradata Aster
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