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

DBMS > Amazon DynamoDB vs. GridGain vs. Milvus vs. Qdrant

System Properties Comparison Amazon DynamoDB vs. GridGain vs. Milvus vs. Qdrant

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameAmazon DynamoDB  Xexclude from comparisonGridGain  Xexclude from comparisonMilvus  Xexclude from comparisonQdrant  Xexclude from comparison
DescriptionHosted, scalable database service by Amazon with the data stored in Amazons cloudGridGain is an in-memory computing platform, built on Apache IgniteA DBMS designed for efficient storage of vector data and vector similarity searchesA high-performance vector database with neural network or semantic-based matching
Primary database modelDocument store
Key-value store
Key-value store
Relational DBMS
Vector DBMSVector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score74.07
Rank#17  Overall
#3  Document stores
#2  Key-value stores
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score2.31
Rank#113  Overall
#3  Vector DBMS
Score1.16
Rank#175  Overall
#6  Vector DBMS
Websiteaws.amazon.com/­dynamodbwww.gridgain.commilvus.iogithub.com/­qdrant/­qdrant
qdrant.tech
Technical documentationdocs.aws.amazon.com/­dynamodbwww.gridgain.com/­docs/­index.htmlmilvus.io/­docs/­overview.mdqdrant.tech/­documentation
DeveloperAmazonGridGain Systems, Inc.Qdrant
Initial release2012200720192021
Current releaseGridGain 8.5.12.3.4, January 2024
License infoCommercial or Open Sourcecommercial infofree tier for a limited amount of database operationscommercialOpen Source infoApache Version 2.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Zilliz Cloud – Cloud-native service for Milvus
Implementation languageJava, C++, .NetC++, GoRust
Server operating systemshostedLinux
OS X
Solaris
Windows
Linux
macOS info10.14 or later
Windows infowith WSL 2 enabled
Docker
Linux
macOS
Windows
Data schemeschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyesVector, Numeric and StringNumbers, Strings, Geo, Boolean
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.yesnono
Secondary indexesyesyesnoyes infoKeywords, numberic ranges, geo, full-text
SQL infoSupport of SQLnoANSI-99 for query and DML statements, subset of DDLnono
APIs and other access methodsRESTful HTTP APIHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
RESTful HTTP APIgRPC
OpenAPI 3.0
RESTful HTTP/JSON API infoOpenAPI 3.0
Supported programming languages.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby
C#
C++
Java
PHP
Python
Ruby
Scala
C++
Go
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
Python
Rust
Server-side scripts infoStored proceduresnoyes (compute grid and cache interceptors can be used instead)no
Triggersyes infoby integration with AWS Lambdayes (cache interceptors and events)no
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes (replicated cache)Collection-level replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)yes (compute grid and hadoop accelerator)nono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency infocan be specified for read operations
Immediate ConsistencyBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Eventual Consistency, tunable consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoACID across one or more tables within a single AWS account and regionACIDno
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.yesyesyes
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)Security Hooks for custom implementationsRole based access control and fine grained access rightsKey-based authentication
More information provided by the system vendor
Amazon DynamoDBGridGainMilvusQdrant
Specific characteristicsMilvus is an open-source and cloud-native vector database built for production-ready...
» more
Competitive advantagesHighly available, versatile, and robust with millisecond latency. Supports batch...
» more
Typical application scenariosRAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more
Key customersMilvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more
Market metricsAs of January 2024, 25k+ GitHub stars 10M+ downloads and installations​ ​ 3k+ enterprise...
» more
Licensing and pricing modelsMilvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

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
3rd partiesCData: Connect to Big Data & NoSQL through standard Drivers.
» more

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

More resources
Amazon DynamoDBGridGainMilvusQdrant
DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

The popularity of cloud-based DBMSs has increased tenfold in four years
7 February 2017, Matthias Gelbmann

Increased popularity for consuming DBMS services out of the cloud
2 October 2015, Paul Andlinger

show all

Vector databases
2 June 2023, Matthias Gelbmann

show all

Recent citations in the news

Freecharge lowered their identity management system cost and improved scaling by switching to Amazon DynamoDB ...
20 May 2024, AWS Blog

Migrating Uber's Ledger Data from DynamoDB to LedgerStore
11 April 2024, Uber

Zendesk Moves from DynamoDB to MySQL and S3 to Save over 80% in Costs
29 December 2023, InfoQ.com

Using the circuit-breaker pattern with AWS Lambda extensions and Amazon DynamoDB | Amazon Web Services
16 May 2024, AWS Blog

Simplify cross-account access control with Amazon DynamoDB using resource-based policies | Amazon Web Services
20 March 2024, AWS Blog

provided by Google News

GridGain's 2023 Growth Positions Company for Strong 2024
25 January 2024, Datanami

GridGain in-memory data and generative AI – Blocks and Files
10 May 2024, Blocks & Files

GridGain Announces Call for Speakers for Virtual Apache Ignite Summit 2024
8 February 2024, PR Newswire

GridGain Adds Andy Sacks as Chief Revenue Officer, Promotes Lalit Ahuja to Chief Customer and Product Officer ...
17 July 2023, Yahoo Finance

GridGain: Product Overview and Analysis
5 June 2019, eWeek

provided by Google News

How NVIDIA GPU Acceleration Supercharged Milvus Vector Database
26 March 2024, The New Stack

AI-Powered Search Engine With Milvus Vector Database on Vultr
31 January 2024, SitePoint

Milvus 2.4 Unveils Game-Changing Features for Enhanced Vector Search
20 March 2024, GlobeNewswire

Zilliz Unveils Game-Changing Features for Vector Search
22 March 2024, Datanami

IBM watsonx.data’s integrated vector database: unify, prepare, and deliver your data for AI
9 April 2024, IBM

provided by Google News

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

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

Qdrant offers managed vector database for hybrid clouds
16 April 2024, InfoWorld

Qdrant launches first vector database as a managed hybrid cloud
16 April 2024, VentureBeat

Why Vector Data Services For AI Are A Moveable Feast
17 April 2024, Forbes

provided by Google News



Share this page

Featured Products

Neo4j logo

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

RaimaDB logo

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

SingleStore logo

The database to transact, analyze and contextualize your data in real time.
Try it today.

Milvus logo

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

Present your product here