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DBMS > GridGain vs. Vertica

System Properties Comparison GridGain vs. Vertica

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Editorial information provided by DB-Engines
NameGridGain  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionGridGain is an in-memory computing platform, built on Apache IgniteCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelKey-value store
Relational DBMS
Relational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.07
Rank#147  Overall
#26  Key-value stores
#68  Relational DBMS
Score13.30
Rank#44  Overall
#28  Relational DBMS
Websitewww.gridgain.comwww.vertica.com
Technical documentationwww.gridgain.com/­docs/­index.htmlvertica.com/­documentation
DeveloperGridGain Systems, Inc.OpenText infopreviously Micro Focus and Hewlett Packard
Initial release20072005
Current releaseGridGain 8.5.112.0.3, January 2023
License infoCommercial or Open Sourcecommercialcommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Vertica Accelerator’s high-performance analytics and machine learning with SQL or Python is available as SaaS.
Implementation languageJava, C++, .NetC++
Server operating systemsLinux
OS X
Solaris
Windows
Linux
Data schemeyesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateyesyes
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 indexesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLANSI-99 for query and DML statements, subset of DDLFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesC#
C++
Java
PHP
Python
Ruby
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresyes (compute grid and cache interceptors can be used instead)yes, PostgreSQL PL/pgSQL, with minor differences
Triggersyes (cache interceptors and events)yes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes (replicated cache)Multi-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes (compute grid and hadoop accelerator)no infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesno
User concepts infoAccess controlSecurity Hooks for custom implementationsfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
GridGainVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more
Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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More resources
GridGainVertica infoOpenText™ Vertica™
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