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DBMS > Apache Impala vs. Atos Standard Common Repository vs. Databricks vs. Microsoft Azure AI Search vs. Teradata

System Properties Comparison Apache Impala vs. Atos Standard Common Repository vs. Databricks vs. Microsoft Azure AI Search vs. Teradata

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonAtos Standard Common Repository  Xexclude from comparisonDatabricks  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparisonTeradata  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 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.Search-as-a-service for web and mobile app developmentA hybrid cloud data analytics software platform (Teradata Vantage)
Primary database modelRelational DBMSDocument store
Key-value store
Document store
Relational DBMS
Search engineRelational DBMS
Secondary database modelsDocument storeVector DBMSDocument store
Graph DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score5.59
Rank#63  Overall
#7  Search engines
Score45.33
Rank#21  Overall
#15  Relational DBMS
Websiteimpala.apache.orgatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.databricks.comazure.microsoft.com/­en-us/­services/­searchwww.teradata.com
Technical documentationimpala.apache.org/­impala-docs.htmldocs.databricks.comlearn.microsoft.com/­en-us/­azure/­searchdocs.teradata.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaAtos Convergence CreatorsDatabricksMicrosoftTeradata
Initial release20132016201320151984
Current release4.1.0, June 20221703V1Teradata Vantage 1.0 MU2, January 2019
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialcommercialcommercial
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++Java
Server operating systemsLinuxLinuxhostedhostedhosted
Linux
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.noyesyesnoyes
Secondary indexesyesyesyesyesyes infoJoin-index to prejoin tables, aggregate index, sparse index, hash index
SQL infoSupport of SQLSQL-like DML and DDL statementsnowith Databricks SQLnoyes infoSQL 2016 + extensions
APIs and other access methodsJDBC
ODBC
LDAPJDBC
ODBC
RESTful HTTP API
RESTful HTTP API.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB
Supported programming languagesAll languages supporting JDBC/ODBCAll languages with LDAP bindingsPython
R
Scala
C#
Java
JavaScript
Python
C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenouser defined functions and aggregatesnoyes infoUDFs, stored procedures, table functions in parallel
Triggersnoyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infocell divisionSharding infoImplicit feature of the cloud serviceSharding infoHashing
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesyesyes infoImplicit feature of the cloud serviceMulti-source replication
Source-replica replication
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 ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic execution of specific operationsACIDnoACID
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.noyesnonoyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosLDAP bind authenticationyes infousing Azure authenticationfine grained access rights according to SQL-standard
More information provided by the system vendor
Apache ImpalaAtos Standard Common RepositoryDatabricksMicrosoft Azure AI SearchTeradata
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Apache ImpalaAtos Standard Common RepositoryDatabricksMicrosoft Azure AI SearchTeradata
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