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

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

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonAtos Standard Common Repository  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparisonTimescaleDB  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 networksMicrosoft 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 developmentA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMSDocument store
Key-value store
Relational DBMSSearch engineTime Series DBMS
Secondary database modelsDocument storeVector DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score14.03
Rank#40  Overall
#24  Relational DBMS
Score105.40
Rank#11  Overall
#8  Relational DBMS
Score5.71
Rank#64  Overall
#8  Search engines
Score4.87
Rank#74  Overall
#4  Time Series DBMS
Websiteimpala.apache.orgatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.microsoft.com/­en-us/­microsoft-365/­accessazure.microsoft.com/­en-us/­services/­searchwww.timescale.com
Technical documentationimpala.apache.org/­impala-docs.htmldeveloper.microsoft.com/­en-us/­accesslearn.microsoft.com/­en-us/­azure/­searchdocs.timescale.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaAtos Convergence CreatorsMicrosoftMicrosoftTimescale
Initial release20132016199220152017
Current release4.1.0, June 202217031902 (16.0.11328.20222), March 2019V12.13.0, November 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercial infoBundled with Microsoft OfficecommercialOpen Source infoApache 2.0
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++C
Server operating systemsLinuxLinuxWindows infoNot a real database server, but making use of DLLshostedLinux
OS X
Windows
Data schemeyesSchema and schema-less with LDAP viewsyesyesyes
Typing infopredefined data types such as float or dateyesoptionalyesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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.noyesnoyes
Secondary indexesyesyesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnoyes infobut not compliant to any SQL standardnoyes infofull PostgreSQL SQL syntax
APIs and other access methodsJDBC
ODBC
LDAPADO.NET
DAO
ODBC
OLE DB
RESTful HTTP APIADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesAll languages supporting JDBC/ODBCAll languages with LDAP bindingsC
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
C#
Java
JavaScript
Python
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenoyes infosince Access 2010 using the ACE-enginenouser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersnoyesyes infosince Access 2010 using the ACE-enginenoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infocell divisionnoneSharding infoImplicit feature of the cloud serviceyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesnoneyes infoImplicit feature of the cloud serviceSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic execution of specific operationsACID infobut no files for transaction loggingnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyes infobut no files for transaction loggingyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnono
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 authenticationfine grained access rights according to SQL-standard

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More resources
Apache ImpalaAtos Standard Common RepositoryMicrosoft AccessMicrosoft Azure AI SearchTimescaleDB
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