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DBMS > HEAVY.AI vs. Microsoft Azure Table Storage vs. Qdrant vs. Yaacomo

System Properties Comparison HEAVY.AI vs. Microsoft Azure Table Storage vs. Qdrant vs. Yaacomo

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Editorial information provided by DB-Engines
NameHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonQdrant  Xexclude from comparisonYaacomo  Xexclude from comparison
Yaacomo seems to be discontinued and is removed from the DB-Engines ranking
DescriptionA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareA Wide Column Store for rapid development using massive semi-structured datasetsA high-performance vector database with neural network or semantic-based matchingOpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database modelRelational DBMSWide column storeVector DBMSRelational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.41
Rank#153  Overall
#71  Relational DBMS
Score3.55
Rank#80  Overall
#6  Wide column stores
Score1.53
Rank#145  Overall
#7  Vector DBMS
Websitegithub.com/­heavyai/­heavydb
www.heavy.ai
azure.microsoft.com/­en-us/­services/­storage/­tablesgithub.com/­qdrant/­qdrant
qdrant.tech
yaacomo.com
Technical documentationdocs.heavy.aiqdrant.tech/­documentation
DeveloperHEAVY.AI, Inc.MicrosoftQdrantQ2WEB GmbH
Initial release2016201220212009
Current release5.10, January 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2; enterprise edition availablecommercialOpen Source infoApache Version 2.0commercial
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++ and CUDARust
Server operating systemsLinuxhostedDocker
Linux
macOS
Windows
Android
Linux
Windows
Data schemeyesschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyesNumbers, Strings, Geo, Booleanyes
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.nononono
Secondary indexesnonoyes infoKeywords, numberic ranges, geo, full-textyes
SQL infoSupport of SQLyesnonoyes
APIs and other access methodsJDBC
ODBC
Thrift
Vega
RESTful HTTP APIgRPC
OpenAPI 3.0
RESTful HTTP/JSON API infoOpenAPI 3.0
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBC/Thrift
Python
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
Python
Rust
Server-side scripts infoStored proceduresnono
Triggersnonoyes
Partitioning methods infoMethods for storing different data on different nodesSharding infoRound robinSharding infoImplicit feature of the cloud serviceShardinghorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replicationyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Collection-level replicationSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency, tunable consistencyImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanooptimistic lockingACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesyes
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights based on private key authentication or shared access signaturesKey-based authenticationfine grained access rights according to SQL-standard

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More resources
HEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Microsoft Azure Table StorageQdrantYaacomo
Recent citations in the news

Dr. Mike Flaxman, VP or Product Management at HEAVY.AI – Interview Series
19 September 2024, Unite.AI

5 Q’s for Mike Flaxman, Vice President of Heavy.AI
15 August 2024, Center for Data Innovation

HEAVY.AI Accelerates Big Data Analytics with Vultr's High-Performance GPU Cloud Infrastructure
11 September 2024, insideBIGDATA

HEAVY.AI Accelerates Big Data Analytics with Vultr’s High-Performance GPU Cloud Infrastructure
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24 April 2024, SiliconANGLE News

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10 July 2024, InfoWorld

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23 July 2024, Simplilearn

Azure Cosmos DB Data Migration tool imports from Azure Table storage
5 May 2015, Microsoft

How to write data to Azure Table Store with an Azure Function
14 April 2017, Experts Exchange

Testing Precompiled Azure Functions Locally with Storage Emulator
8 March 2018, Visual Studio Magazine

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Qdrant review: A highly flexible option for vector search
29 July 2024, InfoWorld

Open source vector database startup Qdrant raises $28M
23 January 2024, TechCrunch

Vector database company Qdrant wants RAG to be more cost-effective
2 July 2024, VentureBeat

Qdrant Announces an Industry-First Hybrid Cloud Offering For Enterprise AI Applications
16 April 2024, Business Wire

Qdrant unveils hybrid vector algorithm for improved RAG
2 July 2024, Blocks & Files

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