DBMS > Firebase Realtime Database vs. Google BigQuery vs. Hawkular Metrics vs. Memgraph vs. Milvus

System Properties Comparison Firebase Realtime Database vs. Google BigQuery vs. Hawkular Metrics vs. Memgraph vs. Milvus

Editorial information provided by DB-Engines

Name

Description

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Large scale data warehouse service with append-only tables

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

An open source graph database built for real-time streaming and compatible with Neo4j

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	15.00
Rank	#39	Overall
	#6	Document stores

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.04
Rank	#374	Overall
	#38	Time Series DBMS

Score	3.00
Rank	#102	Overall
	#8	Graph DBMS

Score	1.81
Rank	#144	Overall
	#5	Vector DBMS

Website

firebase.google.com/products/realtime-database

cloud.google.com/bigquery

www.hawkular.org

memgraph.com

milvus.io

Technical documentation

firebase.google.com/docs/database

cloud.google.com/bigquery/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

memgraph.com/docs

milvus.io/docs/overview.md

Social network pages

Developer

Google

acquired by Google 2014

Google

Community supported by Red Hat

Memgraph Ltd

Initial release

2012

2010

2014

2017

2019

Current release

2.3.4, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Free license available

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++, Go

Server operating systems

hosted

Linux
OS X
Windows

Linux

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Data scheme

schema-free

yes

schema-free

schema-free and schema-optional

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

yes

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

RESTful HTTP/JSON API

HTTP REST

Bolt protocol
Cypher query language

RESTful HTTP API

Supported programming languages

Java
JavaScript
Objective-C

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
Python
Ruby

.Net
C
C++
Elixir
Go
Haskell
Java
JavaScript
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

limited functionality with using 'rules'

user defined functions

in JavaScript

Triggers

Callbacks are triggered when data changes

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

dynamic graph partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Multi-source replication using RAFT

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

yes

Since BigQuery is designed for querying data

ACID

with snapshot isolation

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

with periodic snapshot and write-ahead logging (WAL) of changes

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, based on authentication and database rules

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Users, roles and permissions

Role based access control and fine grained access rights

More information provided by the system vendor

Specific characteristics

Memgraph directly connects to your streaming infrastructure so you and your team...
» more

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

Business Source License ensures a future for the Memgraph community MAGE algorithm...
» more

Highly available, versatile, and robust with millisecond latency. Supports batch...
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Typical application scenarios

Graph algorithms in bioinformatics Social network analysis Cryptocurrency network...
» more

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

You can check out our pricing model and licenses on the company website .
» more

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.