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DBMS > Apache Impala vs. GridGain vs. SWC-DB vs. Valentina Server

System Properties Comparison Apache Impala vs. GridGain vs. SWC-DB vs. Valentina Server

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonGridGain  Xexclude from comparisonSWC-DB infoSuper Wide Column Database  Xexclude from comparisonValentina Server  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopGridGain is an in-memory computing platform, built on Apache IgniteA high performance, scalable Wide Column DBMSObject-relational database and reports server
Primary database modelRelational DBMSKey-value store
Relational DBMS
Wide column storeRelational DBMS
Secondary database modelsDocument storeTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score0.01
Rank#376  Overall
#13  Wide column stores
Score0.17
Rank#327  Overall
#145  Relational DBMS
Websiteimpala.apache.orgwww.gridgain.comgithub.com/­kashirin-alex/­swc-db
www.swcdb.org
www.valentina-db.net
Technical documentationimpala.apache.org/­impala-docs.htmlwww.gridgain.com/­docs/­index.htmlvalentina-db.com/­docs/­dokuwiki/­v5/­doku.php
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaGridGain Systems, Inc.Alex KashirinParadigma Software
Initial release2013200720201999
Current release4.1.0, June 2022GridGain 8.5.10.5, April 20215.7.5
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoGPL V3commercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Java, C++, .NetC++
Server operating systemsLinuxLinux
OS X
Solaris
Windows
LinuxLinux
OS X
Windows
Data schemeyesyesschema-freeyes
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.noyesno
Secondary indexesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsANSI-99 for query and DML statements, subset of DDLSQL-like query languageyes
APIs and other access methodsJDBC
ODBC
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Proprietary protocol
Thrift
ODBC
Supported programming languagesAll languages supporting JDBC/ODBCC#
C++
Java
PHP
Python
Ruby
Scala
C++.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyes (compute grid and cache interceptors can be used instead)noyes
Triggersnoyes (cache interceptors and events)noyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryes (replicated cache)
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyes (compute grid and hadoop accelerator)nono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.noyesnoyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosSecurity Hooks for custom implementationsfine grained access rights according to SQL-standard

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More resources
Apache ImpalaGridGainSWC-DB infoSuper Wide Column DatabaseValentina Server
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