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DBMS > EXASOL vs. GridGain vs. TigerGraph vs. Vertica

System Properties Comparison EXASOL vs. GridGain vs. TigerGraph vs. Vertica

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Editorial information provided by DB-Engines
NameEXASOL  Xexclude from comparisonGridGain  Xexclude from comparisonTigerGraph  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionHigh-performance, in-memory, MPP database specifically designed for in-memory analytics.GridGain is an in-memory computing platform, built on Apache IgniteA complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-timeCloud 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 modelRelational DBMSKey-value store
Relational DBMS
Graph DBMSRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.99
Rank#124  Overall
#58  Relational DBMS
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score1.83
Rank#139  Overall
#13  Graph DBMS
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websitewww.exasol.comwww.gridgain.comwww.tigergraph.comwww.vertica.com
Technical documentationwww.exasol.com/­resourceswww.gridgain.com/­docs/­index.htmldocs.tigergraph.comvertica.com/­documentation
DeveloperExasolGridGain Systems, Inc.OpenText infopreviously Micro Focus and Hewlett Packard
Initial release2000200720172005
Current releaseGridGain 8.5.112.0.3, January 2023
License infoCommercial or Open Sourcecommercialcommercialcommercialcommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenononono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, C++, .NetC++C++
Server operating systemsLinux
OS X
Solaris
Windows
LinuxLinux
Data schemeyesyesyesYes, 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 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.noyesnono
Secondary indexesyesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLyesANSI-99 for query and DML statements, subset of DDLSQL-like query language (GSQL)Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methods.Net
JDBC
ODBC
WebSocket
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesJava
Lua
Python
R
C#
C++
Java
PHP
Python
Ruby
Scala
C++
Java
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresuser defined functionsyes (compute grid and cache interceptors can be used instead)yesyes, PostgreSQL PL/pgSQL, with minor differences
Triggersyesyes (cache interceptors and events)noyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesShardingShardinghorizontal 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 infoHadoop integrationyes (compute grid and hadoop accelerator)yesno infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnoyes infoRelationships in graphsyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACIDACID
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.yesyesnono
User concepts infoAccess controlAccess rights for users, groups and roles according to SQL-standardSecurity Hooks for custom implementationsRole-based access controlfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
EXASOLGridGainTigerGraphVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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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,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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EXASOLGridGainTigerGraphVertica infoOpenText™ Vertica™
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