DB-EnginesExtremeDB: mitigate connectivity issues in a DBMSEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > Amazon DocumentDB vs. HEAVY.AI vs. Microsoft Azure Table Storage

System Properties Comparison Amazon DocumentDB vs. HEAVY.AI vs. Microsoft Azure Table Storage

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceA 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 datasets
Primary database modelDocument storeRelational DBMSWide column store
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#132  Overall
#24  Document stores
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score4.48
Rank#75  Overall
#6  Wide column stores
Websiteaws.amazon.com/­documentdbgithub.com/­heavyai/­heavydb
www.heavy.ai
azure.microsoft.com/­en-us/­services/­storage/­tables
Technical documentationaws.amazon.com/­documentdb/­resourcesdocs.heavy.ai
DeveloperHEAVY.AI, Inc.Microsoft
Initial release201920162012
Current release5.10, January 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; enterprise edition availablecommercial
Cloud-based only infoOnly available as a cloud serviceyesnoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++ and CUDA
Server operating systemshostedLinuxhosted
Data schemeschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyesyes
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.nonono
Secondary indexesyesnono
SQL infoSupport of SQLnoyesno
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)JDBC
ODBC
Thrift
Vega
RESTful HTTP API
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
All languages supporting JDBC/ODBC/Thrift
Python
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresnonono
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infoRound robinSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasMulti-source replicationyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)nono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsnooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesno
User concepts infoAccess controlAccess rights for users and rolesfine grained access rights according to SQL-standardAccess rights based on private key authentication or shared access signatures

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

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

Vector search for Amazon DocumentDB (with MongoDB compatibility) is now generally available | Amazon Web Services
29 November 2023, AWS Blog

Reduce cost and improve performance by migrating to Amazon DocumentDB 5.0 | Amazon Web Services
15 April 2024, AWS Blog

AWS announces Amazon DocumentDB I/O-Optimized
21 November 2023, AWS Blog

AWS announces vector search for Amazon DocumentDB
29 November 2023, AWS Blog

Mask sensitive Amazon DocumentDB log data with Amazon CloudWatch Logs data protection | Amazon Web Services
16 April 2024, AWS Blog

provided by Google News

HEAVY.AI Introduces HeavyIQ, Delivering Powerful Conversational Analytics Focused on Location and Time Data
19 March 2024, Datanami

Big Data Analytics: A Game Changer for Infrastructure
13 July 2023, Spiceworks News and Insights

HEAVY.AI Launches HEAVY 7.0, Introducing Real-Time Machine Learning Capabilities
19 April 2023, Business Wire

Making the most of geospatial intelligence
14 April 2023, InfoWorld

The insideBIGDATA IMPACT 50 List for Q4 2023
11 October 2023, insideBIGDATA

provided by Google News

Azure Cosmos DB Data Migration tool imports from Azure Table storage | Azure updates
5 May 2015, azure.microsoft.com

How to Use C# Azure.Data.Tables SDK with Azure Cosmos DB
9 July 2021, hackernoon.com

How to use Azure Table storage in .Net
14 January 2019, InfoWorld

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

provided by Google News



Share this page

Featured Products

SingleStore logo

Build AI apps with Vectors on SQL and JSON with milliseconds response times.
Try it today.

RaimaDB logo

RaimaDB, embedded database for mission-critical applications. When performance, footprint and reliability matters.
Try RaimaDB for free.

Milvus logo

Vector database designed for GenAI, fully equipped for enterprise implementation.
Try Managed Milvus for Free

Datastax Astra logo

Bring all your data to Generative AI applications with vector search enabled by the most scalable
vector database available.
Try for Free

Neo4j logo

See for yourself how a graph database can make your life easier.
Use Neo4j online for free.

Present your product here