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DBMS > GridGain vs. HBase vs. MarkLogic vs. Spark SQL vs. Teradata Aster

System Properties Comparison GridGain vs. HBase vs. MarkLogic vs. Spark SQL vs. Teradata Aster

Editorial information provided by DB-Engines
NameGridGain  Xexclude from comparisonHBase  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.
DescriptionGridGain is an in-memory computing platform, built on Apache IgniteWide-column store based on Apache Hadoop and on concepts of BigTableOperational 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 modelKey-value store
Relational DBMS
Wide column storeDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Relational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.55
Rank#150  Overall
#26  Key-value stores
#70  Relational DBMS
Score27.97
Rank#26  Overall
#2  Wide column stores
Score5.18
Rank#63  Overall
#11  Document stores
#1  Native XML DBMS
#1  RDF stores
#7  Search engines
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitewww.gridgain.comhbase.apache.orgwww.marklogic.comspark.apache.org/­sql
Technical documentationwww.gridgain.com/­docs/­index.htmlhbase.apache.org/­book.htmldocs.marklogic.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperGridGain Systems, Inc.Apache Software Foundation infoApache top-level project, originally developed by PowersetMarkLogic Corp.Apache Software FoundationTeradata
Initial release20072008200120142005
Current releaseGridGain 8.5.12.3.4, January 202111.0, December 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache version 2commercial inforestricted free version is availableOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, C++, .NetJavaC++Scala
Server operating systemsLinux
OS X
Solaris
Windows
Linux
Unix
Windows infousing Cygwin
Linux
OS X
Windows
Linux
OS X
Windows
Linux
Data schemeyesschema-free, schema definition possibleschema-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 dateyesoptions to bring your own types, AVROyesyesyes
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.yesnoyesnoyes infoin Aster File Store
Secondary indexesyesnoyesnoyes
SQL infoSupport of SQLANSI-99 for query and DML statements, subset of DDLnoyes infoSQL92SQL-like DML and DDL statementsyes
APIs and other access methodsHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Java API
RESTful HTTP API
Thrift
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 languagesC#
C++
Java
PHP
Python
Ruby
Scala
C
C#
C++
Groovy
Java
PHP
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 proceduresyes (compute grid and cache interceptors can be used instead)yes infoCoprocessors in Javayes infovia XQuery or JavaScriptnoR packages
Triggersyes (cache interceptors and events)yesyesnono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingyes, utilizing Spark CoreSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes (replicated cache)Multi-source replication
Source-replica replication
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 (compute grid and hadoop accelerator)yesyes 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 ConsistencyImmediate Consistency or Eventual ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDSingle row ACID (across millions of columns)ACID infocan act as a resource manager in an XA/JTA transactionnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes, with Range Indexesnono
User concepts infoAccess controlSecurity Hooks for custom implementationsAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACRole-based access control at the document and subdocument levelsnofine grained access rights according to SQL-standard

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More resources
GridGainHBaseMarkLogicSpark SQLTeradata Aster
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