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DBMS > Heroic vs. OmniSci

System Properties Comparison Heroic vs. OmniSci

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Editorial information provided by DB-Engines
NameHeroic  Xexclude from comparisonOmniSci infoFormerly named 'MapD', rebranded to 'OmniSci' in September 2018  Xexclude from comparison
DescriptionTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware
Primary database modelTime Series DBMSRelational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.04
Rank#323  Overall
#29  Time Series DBMS
Score1.26
Rank#154  Overall
#75  Relational DBMS
Websitespotify.github.io/­heroic/­#!/­indexgithub.com/­omnisci/­omniscidb
www.omnisci.com
Technical documentationspotify.github.io/­heroic/­#!/­docs/­overviewdocs.omnisci.com
DeveloperSpotifyMapD Technologies, Inc.
Initial release20142016
Current release5.6, April 2021
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache Version 2; enterprise edition available
Cloud-based only infoOnly available as a cloud servicenono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++ and CUDA
Server operating systemsLinux
Data schemeschema-freeyes
Typing infopredefined data types such as float or dateyesyes
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.nono
Secondary indexesyes infovia Elasticsearchno
SQL infoSupport of SQLnoyes
APIs and other access methodsHQL (Heroic Query Language, a JSON-based language)
HTTP API
JDBC
ODBC
Thrift
Vega
Supported programming languagesAll languages supporting JDBC/ODBC/Thrift
Python
Server-side scripts infoStored proceduresnono
Triggersnono
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoRound robin
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlfine grained access rights according to SQL-standard

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More resources
HeroicOmniSci infoFormerly named 'MapD', rebranded to 'OmniSci' in September 2018
Recent citations in the news

USD 361.56 million growth for GPU Database Market from 2020 to 2025 |Evolving Opportunities with BlazingSQL Inc., Brytlyt Ltd. & Hetero DB Co. Ltd.|17000+ Technavio Reports
26 November 2021, Yahoo Finance

OmniSci Announces Multiple Opportunities To Accelerate Geospatial, GPU Database Knowledge At GTC 2021, April 12-16
12 April 2021, PRNewswire

Incredible Growth of GPU Database Market by 2028 | Kinetica, Omnisci, Sqream
4 October 2021, Global Banking And Finance Review

Review: OmniSci GPU database lifts huge data sets
1 April 2019, InfoWorld

OmniSci Drops OmniSci Free Edition Analytics and Data Science Platform
9 March 2021, Solutions Review

provided by Google News

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