DBMS > IBM Cloudant vs. Microsoft Azure Synapse Analytics vs. Milvus vs. OrigoDB vs. Splice Machine

System Properties Comparison IBM Cloudant vs. Microsoft Azure Synapse Analytics vs. Milvus vs. OrigoDB vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

Database as a Service offering based on Apache CouchDB

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

A fully ACID in-memory object graph database

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Document store

Relational DBMS

Vector DBMS

Document store
Object oriented DBMS

Relational DBMS

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

Score	2.73
Rank	#108	Overall
	#20	Document stores

Score	22.71
Rank	#30	Overall
	#18	Relational DBMS

Score	1.81
Rank	#144	Overall
	#5	Vector DBMS

Score	0.03
Rank	#378	Overall
	#51	Document stores
	#18	Object oriented DBMS

Score	0.54
Rank	#255	Overall
	#116	Relational DBMS

Website

www.ibm.com/products/cloudant

azure.microsoft.com/services/synapse-analytics

milvus.io

origodb.com

splicemachine.com

Technical documentation

cloud.ibm.com/docs/Cloudant

docs.microsoft.com/azure/synapse-analytics

milvus.io/docs/overview.md

origodb.com/docs

splicemachine.com/how-it-works

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Microsoft

Robert Friberg et al

Splice Machine

Initial release

2010

2016

2019

2009

under the name LiveDB

2014

Current release

2.3.4, January 2024

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

AGPL 3.0, commercial license available

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

Erlang

C++

C++, Go

Java

Server operating systems

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

User defined using .NET types and collections

yes

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

RESTful HTTP API

.NET Client API
HTTP API
LINQ

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

C#
Java
PHP

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

.Net

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

Transact SQL

yes

Java

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Sharding

horizontal partitioning

client side managed; servers are not synchronized

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

depending on model

yes

Transaction concepts

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

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

Write ahead log

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 database

yes

Role based access control and fine grained access rights

Role based authorization

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
» more

Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
» more

Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more

Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more

Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
» more

Licensing and pricing models

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

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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