DBMS > Datastax Enterprise vs. EJDB vs. Milvus

System Properties Comparison Datastax Enterprise vs. EJDB vs. Milvus

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

Primary database model

Wide column store

Document store

Vector DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

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

Score	4.00
Rank	#66	Overall
	#4	Wide column stores

Score	0.12
Rank	#339	Overall
	#47	Document stores

Score	2.92
Rank	#87	Overall
	#7	Vector DBMS

Website

www.datastax.com/products/datastax-enterprise

github.com/Softmotions/ejdb

milvus.io

Technical documentation

docs.datastax.com

github.com/Softmotions/ejdb/blob/master/README.md

milvus.io/docs/overview.md

Developer

DataStax

Softmotions

Initial release

2011

2012

2019

Current release

6.8, April 2020

2.4.4, May 2024

License

Commercial or Open Source

commercial

Open Source

GPLv2

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++, Go

Server operating systems

Linux
OS X

server-less

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements (CQL); Spark SQL

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

in-process shared library

RESTful HTTP API

Supported programming languages

C
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

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

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware, advanced replication for edge computing

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

Atomicity and isolation are supported for single operations

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

Access rights for users can be defined per object

Role based access control and fine grained access rights

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

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

show all

Recent citations in the news

DataStax partners with OpenSearch to enhance generative AI search
16 May 2025, IT Brief Australia

IBM to acquire DataStax, helping clients bring the power of unstructured data to enterprise AI applications
25 February 2025, IBM

Big Blue To Acquire Datastax in Enterprise AI Play
28 February 2025, Redmond Channel Partner

IBM to Acquire DataStax to Power Generative AI, Tapping into 93% of Enterprise Data
26 February 2025, eWEEK

IBM To Buy DataStax, Expand Watsonx AI Portfolio’s Data Management Capabilities
25 February 2025, CRN Magazine

provided by Google News

What Is Milvus? A Distributed Vector Database
3 March 2025, Oracle

Milvus 2.5 Creates the Best of Both Worlds With Hybrid Vector-Keyword Search
17 December 2024, GlobeNewswire

AI-Powered Search Engine With Milvus Vector Database on Vultr
7 November 2024, SitePoint

Hands-On Vector Similarity Search with Milvus
20 October 2024, Packt

Dance between dense and sparse embeddings: Enabling Hybrid Search in LangChain-Milvus
19 November 2024, Towards Data Science

provided by Google News

Share this page