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DBMS > Apache Pinot vs. BigObject vs. eXtremeDB vs. GridDB vs. Vertica

System Properties Comparison Apache Pinot vs. BigObject vs. eXtremeDB vs. GridDB vs. Vertica

Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonBigObject  Xexclude from comparisoneXtremeDB  Xexclude from comparisonGridDB  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencyAnalytic DBMS for real-time computations and queriesNatively in-memory DBMS with options for persistency, high-availability and clusteringScalable in-memory time series database optimized for IoT and Big DataCloud 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 DBMSRelational DBMS infoa hierachical model (tree) can be imposedRelational DBMS
Time Series DBMS
Time Series DBMSRelational DBMS infoColumn oriented
Secondary database modelsKey-value store
Relational DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.38
Rank#275  Overall
#126  Relational DBMS
Score0.19
Rank#329  Overall
#146  Relational DBMS
Score0.80
Rank#214  Overall
#99  Relational DBMS
#18  Time Series DBMS
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score10.06
Rank#42  Overall
#26  Relational DBMS
Websitepinot.apache.orgbigobject.iowww.mcobject.comgriddb.netwww.vertica.com
Technical documentationdocs.pinot.apache.orgdocs.bigobject.iowww.mcobject.com/­docs/­extremedb.htmdocs.griddb.netvertica.com/­documentation
DeveloperApache Software Foundation and contributorsBigObject, Inc.McObjectToshiba CorporationOpenText infopreviously Micro Focus and Hewlett Packard
Initial release20152015200120132005
Current release1.0.0, September 20238.2, 20215.1, August 202212.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial infofree community edition availablecommercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonononono 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.
Implementation languageJavaC and C++C++C++
Server operating systemsAll OS with a Java JDK11 or higherLinux infodistributed as a docker-image
OS X infodistributed as a docker-image (boot2docker)
Windows infodistributed as a docker-image (boot2docker)
AIX
HP-UX
Linux
macOS
Solaris
Windows
LinuxLinux
Data schemeyesyesyesyesYes, 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 infonumerical, string, blob, geometry, boolean, timestampyes
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.nono infosupport of XML interfaces availablenono
Secondary indexesyesyesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLSQL-like query languageSQL-like DML and DDL statementsyes infowith the option: eXtremeSQLSQL92, SQL-like TQL (Toshiba Query Language)Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJDBCfluentd
ODBC
RESTful HTTP API
.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesGo
Java
Python
.Net
C
C#
C++
Java
Lua
Python
Scala
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresLuayesnoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnoyes infoby defining eventsyesyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningnonehorizontal partitioning / shardingShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnoneActive Replication Fabric™ for IoT
Multi-source replication infoby means of eXtremeDB Cluster option
Source-replica replication infoby means of eXtremeDB High Availability option
Source-replica replicationMulti-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 methodsnonoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsno infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyImmediate consistency within container, eventual consistency across containersImmediate Consistency
Foreign keys infoReferential integrityyes infoautomatically between fact table and dimension tablesyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID at container levelACID
Concurrency infoSupport for concurrent manipulation of datayes infoRead/write lock on objects (tables, trees)yes infoOptimistic (MVCC) and pessimistic (locking) strategies availableyesyes
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.yesyesyesno
User concepts infoAccess controlnoAccess rights for users can be defined per databasefine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
Apache PinotBigObjecteXtremeDBGridDBVertica infoOpenText™ Vertica™
Specific characteristicseXtremeDB is an in-memory and/or persistent database system that offers an ultra-small...
» more
GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more
Competitive advantageseXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
» more
1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
» more
Typical application scenariosIoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
» more
Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more
Key customersSchneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
» more
Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Market metricsWith hundreds of customers and over 30 million devices/applications using the product...
» more
GitHub trending repository
» more
Licensing and pricing modelsFor server use cases, there is a simple per-server license irrespective of the number...
» more
Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more
Cost-based models and subscription-based models are both available. One license is...
» more

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Apache PinotBigObjecteXtremeDBGridDBVertica infoOpenText™ Vertica™
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