DBMS > Kdb vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Synapse Analytics vs. Milvus

System Properties Comparison Kdb vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Synapse Analytics vs. Milvus

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Editorial information provided by DB-Engines

Name

Description

High performance Time Series DBMS

Globally distributed, horizontally scalable, multi-model database service

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

A DBMS designed for efficient storage of vector data and vector similarity searches

Primary database model

Time Series DBMS
Vector DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Vector DBMS

Secondary database models

Relational DBMS

Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	7.73
Rank	#45	Overall
	#2	Time Series DBMS
	#1	Vector DBMS

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Score	3.01
Rank	#89	Overall
	#4	Vector DBMS

Website

kx.com

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/services/synapse-analytics

milvus.io

Technical documentation

code.kx.com

learn.microsoft.com/azure/cosmos-db

docs.microsoft.com/azure/synapse-analytics

milvus.io/docs/overview.md

Developer

Kx Systems, a division of First Derivatives plc

Microsoft

Initial release

2000

kdb was released 2000, kdb+ in 2003

2014

2016

2019

Current release

3.6, May 2018

2.4.4, May 2024

License

Commercial or Open Source

commercial

free 32-bit version

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C++

C++, Go

Server operating systems

Linux
OS X
Solaris
Windows

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

yes

Vector, Numeric and String

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

table attribute 'grouped'

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language (q)

SQL-like query language

yes

APIs and other access methods

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

ADO.NET
JDBC
ODBC

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C#
Java
PHP

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

JavaScript

Transact SQL

Triggers

yes

with views

JavaScript

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Implicit feature of the cloud service

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Implicit feature of the cloud service

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

rights management via user accounts

Access rights can be defined down to the item level

yes

Role based access control and fine grained access rights

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
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Milvus is an open-source and cloud-native vector database built for production-ready...
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Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Highly available, versatile, and robust with millisecond latency. Supports batch...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
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RAG: retrieval augmented generation Video media : video understanding, video deduplication....
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Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
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Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
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Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
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Licensing and pricing models

upon request
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Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
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