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DBMS > Apache Impala vs. Atos Standard Common Repository vs. Graph Engine vs. Microsoft Access vs. Microsoft Azure AI Search

System Properties Comparison Apache Impala vs. Atos Standard Common Repository vs. Graph Engine vs. Microsoft Access vs. Microsoft Azure AI Search

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonAtos Standard Common Repository  Xexclude from comparisonGraph Engine infoformer name: Trinity  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonMicrosoft Azure AI Search  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 networksA distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engineMicrosoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries. infoThe Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)Search-as-a-service for web and mobile app development
Primary database modelRelational DBMSDocument store
Key-value store
Graph DBMS
Key-value store
Relational DBMSSearch engine
Secondary database modelsDocument storeVector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score14.03
Rank#40  Overall
#24  Relational DBMS
Score0.62
Rank#240  Overall
#21  Graph DBMS
#35  Key-value stores
Score105.40
Rank#11  Overall
#8  Relational DBMS
Score5.71
Rank#64  Overall
#8  Search engines
Websiteimpala.apache.orgatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.graphengine.iowww.microsoft.com/­en-us/­microsoft-365/­accessazure.microsoft.com/­en-us/­services/­search
Technical documentationimpala.apache.org/­impala-docs.htmlwww.graphengine.io/­docs/­manualdeveloper.microsoft.com/­en-us/­accesslearn.microsoft.com/­en-us/­azure/­search
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaAtos Convergence CreatorsMicrosoftMicrosoftMicrosoft
Initial release20132016201019922015
Current release4.1.0, June 202217031902 (16.0.11328.20222), March 2019V1
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoMIT Licensecommercial infoBundled with Microsoft Officecommercial
Cloud-based only infoOnly available as a cloud servicenonononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Java.NET and CC++
Server operating systemsLinuxLinux.NETWindows infoNot a real database server, but making use of DLLshosted
Data schemeyesSchema and schema-less with LDAP viewsyesyesyes
Typing infopredefined data types such as float or dateyesoptionalyesyesyes
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 indexesyesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnonoyes infobut not compliant to any SQL standardno
APIs and other access methodsJDBC
ODBC
LDAPRESTful HTTP APIADO.NET
DAO
ODBC
OLE DB
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCAll languages with LDAP bindingsC#
C++
F#
Visual Basic
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
C#
Java
JavaScript
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenoyesyes infosince Access 2010 using the ACE-engineno
Triggersnoyesnoyes infosince Access 2010 using the ACE-engineno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infocell divisionhorizontal partitioningnoneSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesnoneyes infoImplicit feature of the cloud service
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynononoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic execution of specific operationsnoACID infobut no files for transaction loggingno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesoptional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storageyes infobut no files for transaction loggingyes
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 authenticationno infoa simple user-level security was built in till version Access 2003yes infousing Azure authentication

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More resources
Apache ImpalaAtos Standard Common RepositoryGraph Engine infoformer name: TrinityMicrosoft AccessMicrosoft Azure AI Search
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