DBMS > Apache IoTDB vs. Coveo vs. Firebird vs. Microsoft Azure Synapse Analytics vs. Milvus

System Properties Comparison Apache IoTDB vs. Coveo vs. Firebird vs. Microsoft Azure Synapse Analytics vs. Milvus

Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

Firebird is an open source RDBMS forked from Borland's InterBase

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

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	2.28
Rank	#114	Overall
	#11	Search engines

Score	20.82
Rank	#30	Overall
	#18	Relational DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Website

iotdb.apache.org

www.coveo.com

www.firebirdsql.org

azure.microsoft.com/services/synapse-analytics

milvus.io

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.coveo.com

www.firebirdsql.org/en/reference-manuals

docs.microsoft.com/azure/synapse-analytics

milvus.io/docs/overview.md

Developer

Apache Software Foundation

Coveo

Firebird Foundation

Microsoft

Initial release

2018

2012

2000

As fork of Borland's InterBase

2016

2019

Current release

1.1.0, April 2023

5.0.0, January 2024

2.3.4, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Initial Developer's Public License

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

Java

C and C++

C++

C++, Go

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Data scheme

yes

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

RESTful HTTP API

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

ADO.NET
JDBC
ODBC

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
Java
JavaScript
Python

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

C#
Java
PHP

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

Server-side scripts

Stored procedures

yes

PSQL

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

yes

none

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Features a multi-generational MVCC architecture, readers do not block writers

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

yes

granular access controls, API key management, content filters

Users with fine-grained authorization concept

yes

Role based access control and fine grained access rights

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

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

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