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DBMS > Atos Standard Common Repository vs. GigaSpaces vs. GridDB vs. Vertica vs. WakandaDB

System Properties Comparison Atos Standard Common Repository vs. GigaSpaces vs. GridDB vs. Vertica vs. WakandaDB

Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonGigaSpaces  Xexclude from comparisonGridDB  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparisonWakandaDB  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.
DescriptionHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksHigh performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented TransactionsScalable 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.WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data
Primary database modelDocument store
Key-value store
Document store
Object oriented DBMS infoValues are user defined objects
Time Series DBMSRelational DBMS infoColumn orientedObject oriented DBMS
Secondary database modelsGraph DBMS
Search engine
Key-value store
Relational DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.03
Rank#188  Overall
#32  Document stores
#6  Object oriented DBMS
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score10.06
Rank#42  Overall
#26  Relational DBMS
Score0.10
Rank#356  Overall
#16  Object oriented DBMS
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.gigaspaces.comgriddb.netwww.vertica.comwakanda.github.io
Technical documentationdocs.gigaspaces.com/­latest/­landing.htmldocs.griddb.netvertica.com/­documentationwakanda.github.io/­doc
DeveloperAtos Convergence CreatorsGigaspaces TechnologiesToshiba CorporationOpenText infopreviously Micro Focus and Hewlett PackardWakanda SAS
Initial release20162000201320052012
Current release170315.5, September 20205.1, August 202212.0.3, January 20232.7.0 (April 29, 2019), April 2019
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; Commercial licenses availableOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercial infoLimited community edition freeOpen Source infoAGPLv3, extended commercial license available
Cloud-based only infoOnly available as a cloud servicenononono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containersno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJava, C++, .NetC++C++C++, JavaScript
Server operating systemsLinuxLinux
macOS
Solaris
Windows
LinuxLinuxLinux
OS X
Windows
Data schemeSchema and schema-less with LDAP viewsschema-freeyesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.yes
Typing infopredefined data types such as float or dateoptionalyesyes infonumerical, string, blob, geometry, boolean, timestampyesyes
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 infoXML can be used for describing objects metadatanonono
Secondary indexesyesyesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnoSQL-99 for query and DML statementsSQL92, SQL-like TQL (Toshiba Query Language)Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.no
APIs and other access methodsLDAPGigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
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
RESTful HTTP API
Supported programming languagesAll languages with LDAP bindings.Net
C++
Java
Python
Scala
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
JavaScript
Server-side scripts infoStored proceduresnoyesnoyes, PostgreSQL PL/pgSQL, with minor differencesyes
Triggersyesyes, event driven architectureyesyes, called Custom Alertsyes
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionShardingShardinghorizontal partitioning, hierarchical partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
Source-replica replicationMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoMap-Reduce pattern can be built with XAP task executorsConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsno infoBi-directional Spark integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency infoConsistency level configurable: ALL, QUORUM, ANYImmediate consistency within container, eventual consistency across containersImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsACIDACID at container levelACIDACID
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.yesyesyesnono
User concepts infoAccess controlLDAP bind authenticationRole-based access controlAccess rights for users can be defined per databasefine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hashyes
More information provided by the system vendor
Atos Standard Common RepositoryGigaSpacesGridDBVertica infoOpenText™ Vertica™WakandaDB
Specific characteristicsGridDB 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 advantages1. 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 scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more
Key customersDenso 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 metricsGitHub trending repository
» more
Licensing and pricing modelsOpen 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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More resources
Atos Standard Common RepositoryGigaSpacesGridDBVertica infoOpenText™ Vertica™WakandaDB
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