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DBMS > Apache Impala vs. Atos Standard Common Repository vs. GridDB vs. Microsoft Azure AI Search vs. TerminusDB

System Properties Comparison Apache Impala vs. Atos Standard Common Repository vs. GridDB vs. Microsoft Azure AI Search vs. TerminusDB

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonAtos Standard Common Repository  Xexclude from comparisonGridDB  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparisonTerminusDB infoformer name was DataChemist  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.
DescriptionAnalytic DBMS for HadoopHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksScalable in-memory time series database optimized for IoT and Big DataSearch-as-a-service for web and mobile app developmentScalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships
Primary database modelRelational DBMSDocument store
Key-value store
Time Series DBMSSearch engineGraph DBMS
Secondary database modelsDocument storeKey-value store
Relational DBMS
Vector DBMSDocument store
RDF store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score5.59
Rank#63  Overall
#7  Search engines
Score0.17
Rank#325  Overall
#29  Graph DBMS
Websiteimpala.apache.orgatos.net/en/convergence-creators/portfolio/standard-common-repositorygriddb.netazure.microsoft.com/­en-us/­services/­searchterminusdb.com
Technical documentationimpala.apache.org/­impala-docs.htmldocs.griddb.netlearn.microsoft.com/­en-us/­azure/­searchterminusdb.github.io/­terminusdb/­#
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaAtos Convergence CreatorsToshiba CorporationMicrosoftDataChemist Ltd.
Initial release20132016201320152018
Current release4.1.0, June 202217035.1, August 2022V111.0.0, January 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercialOpen Source infoGPL V3
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++JavaC++Prolog, Rust
Server operating systemsLinuxLinuxLinuxhostedLinux
Data schemeyesSchema and schema-less with LDAP viewsyesyesyes
Typing infopredefined data types such as float or dateyesoptionalyes 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.noyesnonono
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnoSQL92, SQL-like TQL (Toshiba Query Language)noSQL-like query language (WOQL)
APIs and other access methodsJDBC
ODBC
LDAPJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
RESTful HTTP APIOWL
RESTful HTTP API
WOQL (Web Object Query Language)
Supported programming languagesAll languages supporting JDBC/ODBCAll languages with LDAP bindingsC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C#
Java
JavaScript
Python
JavaScript
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenononoyes
Triggersnoyesyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infocell divisionShardingSharding infoImplicit feature of the cloud serviceGraph Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesSource-replica replicationyes infoImplicit feature of the cloud serviceJournaling Streams
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate consistency within container, eventual consistency across containersImmediate Consistency
Foreign keys infoReferential integritynonononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic execution of specific operationsACID at container levelnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes infoin-memory journaling
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyesno
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosLDAP bind authenticationAccess rights for users can be defined per databaseyes infousing Azure authenticationRole-based access control
More information provided by the system vendor
Apache ImpalaAtos Standard Common RepositoryGridDBMicrosoft Azure AI SearchTerminusDB infoformer name was DataChemist
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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Apache ImpalaAtos Standard Common RepositoryGridDBMicrosoft Azure AI SearchTerminusDB infoformer name was DataChemist
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