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DBMS > Apache Impala vs. Atos Standard Common Repository vs. Databricks vs. Greenplum vs. Hive

System Properties Comparison Apache Impala vs. Atos Standard Common Repository vs. Databricks vs. Greenplum vs. Hive

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonAtos Standard Common Repository  Xexclude from comparisonDatabricks  Xexclude from comparisonGreenplum  Xexclude from comparisonHive  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.Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database infoA logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.data warehouse software for querying and managing large distributed datasets, built on Hadoop
Primary database modelRelational DBMSDocument store
Key-value store
Document store
Relational DBMS
Relational DBMSRelational DBMS
Secondary database modelsDocument storeDocument store
Spatial 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
Score8.37
Rank#48  Overall
#30  Relational DBMS
Score61.17
Rank#18  Overall
#12  Relational DBMS
Websiteimpala.apache.orgatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.databricks.comgreenplum.orghive.apache.org
Technical documentationimpala.apache.org/­impala-docs.htmldocs.databricks.comdocs.greenplum.orgcwiki.apache.org/­confluence/­display/­Hive/­Home
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaAtos Convergence CreatorsDatabricksPivotal Software Inc.Apache Software Foundation infoinitially developed by Facebook
Initial release20132016201320052012
Current release4.1.0, June 202217037.0.0, September 20233.1.3, April 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialOpen Source infoApache 2.0Open Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaJava
Server operating systemsLinuxLinuxhostedLinuxAll OS with a Java VM
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.noyesyesyes infosince Version 4.2
Secondary indexesyesyesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnowith Databricks SQLyesSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
LDAPJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
JDBC
ODBC
Thrift
Supported programming languagesAll languages supporting JDBC/ODBCAll languages with LDAP bindingsPython
R
Scala
C
Java
Perl
Python
R
C++
Java
PHP
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenouser defined functions and aggregatesyesyes infouser defined functions and integration of map-reduce
Triggersnoyesyesno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infocell divisionShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesyesSource-replica replicationselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyesyes infoquery execution via MapReduce
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic execution of specific operationsACIDACIDno
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.noyesnono
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosLDAP bind authenticationfine grained access rights according to SQL-standardAccess rights for users, groups and roles
More information provided by the system vendor
Apache ImpalaAtos Standard Common RepositoryDatabricksGreenplumHive
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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Apache ImpalaAtos Standard Common RepositoryDatabricksGreenplumHive
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