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DBMS > Apache Impala vs. Atos Standard Common Repository vs. Databricks vs. GeoSpock vs. Microsoft Access

System Properties Comparison Apache Impala vs. Atos Standard Common Repository vs. Databricks vs. GeoSpock vs. Microsoft Access

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonAtos Standard Common Repository  Xexclude from comparisonDatabricks  Xexclude from comparisonGeoSpock  Xexclude from comparisonMicrosoft Access  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.
DescriptionAnalytic DBMS for HadoopHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.Spatial and temporal data processing engine for extreme data scaleMicrosoft 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.)
Primary database modelRelational DBMSDocument store
Key-value store
Document store
Relational DBMS
Relational DBMSRelational DBMS
Secondary database modelsDocument storeTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score101.16
Rank#11  Overall
#8  Relational DBMS
Websiteimpala.apache.orgatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.databricks.comgeospock.comwww.microsoft.com/­en-us/­microsoft-365/­access
Technical documentationimpala.apache.org/­impala-docs.htmldocs.databricks.comdeveloper.microsoft.com/­en-us/­access
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaAtos Convergence CreatorsDatabricksGeoSpockMicrosoft
Initial release2013201620131992
Current release4.1.0, June 202217032.0, September 20191902 (16.0.11328.20222), March 2019
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialcommercialcommercial infoBundled with Microsoft Office
Cloud-based only infoOnly available as a cloud servicenonoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++JavaJava, JavascriptC++
Server operating systemsLinuxLinuxhostedhostedWindows infoNot a real database server, but making use of DLLs
Data schemeyesSchema and schema-less with LDAP viewsFlexible Schema (defined schema, partial schema, schema free)yesyes
Typing infopredefined data types such as float or dateyesoptionalyesyes
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.noyesyesno
Secondary indexesyesyesyestemporal, categoricalyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnowith Databricks SQLANSI SQL for query only (using Presto)yes infobut not compliant to any SQL standard
APIs and other access methodsJDBC
ODBC
LDAPJDBC
ODBC
RESTful HTTP API
JDBCADO.NET
DAO
ODBC
OLE DB
Supported programming languagesAll languages supporting JDBC/ODBCAll languages with LDAP bindingsPython
R
Scala
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenouser defined functions and aggregatesnoyes infosince Access 2010 using the ACE-engine
Triggersnoyesnoyes infosince Access 2010 using the ACE-engine
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infocell divisionAutomatic shardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesyesnone
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 ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic execution of specific operationsACIDnoACID infobut no files for transaction logging
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes infobut no files for transaction logging
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 authenticationAccess rights for users can be defined per tableno infoa simple user-level security was built in till version Access 2003
More information provided by the system vendor
Apache ImpalaAtos Standard Common RepositoryDatabricksGeoSpockMicrosoft Access
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Apache ImpalaAtos Standard Common RepositoryDatabricksGeoSpockMicrosoft Access
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