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DBMS > Apache Impala vs. HEAVY.AI vs. Kinetica vs. Teradata Aster vs. Transwarp Hippo

System Properties Comparison Apache Impala vs. HEAVY.AI vs. Kinetica vs. Teradata Aster vs. Transwarp Hippo

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonKinetica  Xexclude from comparisonTeradata Aster  Xexclude from comparisonTranswarp Hippo  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionAnalytic DBMS for HadoopA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareFully vectorized database across both GPUs and CPUsPlatform for big data analytics on multistructured data sources and typesCloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets
Primary database modelRelational DBMSRelational DBMSRelational DBMSRelational DBMSVector DBMS
Secondary database modelsDocument storeSpatial DBMSSpatial 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
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score0.00
Rank#383  Overall
#13  Vector DBMS
Websiteimpala.apache.orggithub.com/­heavyai/­heavydb
www.heavy.ai
www.kinetica.comwww.transwarp.cn/­en/­subproduct/­hippo
Technical documentationimpala.apache.org/­impala-docs.htmldocs.heavy.aidocs.kinetica.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaHEAVY.AI, Inc.KineticaTeradata
Initial release20132016201220052023
Current release4.1.0, June 20225.10, January 20227.1, August 20211.0, May 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache Version 2; enterprise edition availablecommercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++ and CUDAC, C++C++
Server operating systemsLinuxLinuxLinuxLinuxLinux
macOS
Data schemeyesyesyesFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
Typing infopredefined data types such as float or dateyesyesyesyesVector, Numeric and String
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.nononoyes infoin Aster File Storeno
Secondary indexesyesnoyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsyesSQL-like DML and DDL statementsyesno
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
Thrift
Vega
JDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
ODBC
OLE DB
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCAll languages supporting JDBC/ODBC/Thrift
Python
C++
Java
JavaScript (Node.js)
Python
C
C#
C++
Java
Python
R
C++
Java
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenouser defined functionsR packagesno
Triggersnonoyes infotriggers when inserted values for one or more columns fall within a specified rangenono
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoRound robinShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorMulti-source replicationSource-replica replicationyes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenonoyes infoSQL Map-Reduce Frameworkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynonoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononoACIDno
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.noyesyes infoGPU vRAM or System RAMnoyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosfine grained access rights according to SQL-standardAccess rights for users and roles on table levelfine grained access rights according to SQL-standardRole based access control and fine grained access rights

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More resources
Apache ImpalaHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022KineticaTeradata AsterTranswarp Hippo
Recent citations in the news

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

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Northwestern Analytics Partners with Teradata Aster to Host Hackathon
23 May 2014, Northwestern Engineering

Teradata Aster gets graph database, HDFS-compatible file store
8 October 2013, ZDNet

Teradata Provides the Simplest Way to Bring the Science of Data to the Art of Business
22 September 2011, PR Newswire

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23 April 2015, The Register

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