DBMS > Firebase Realtime Database vs. Microsoft Azure Synapse Analytics vs. Qdrant vs. Weaviate

System Properties Comparison Firebase Realtime Database vs. Microsoft Azure Synapse Analytics vs. Qdrant vs. Weaviate

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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.

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

A high-performance vector database with neural network or semantic-based matching

An AI-native realtime vector database engine that integrates scalable machine learning models.

Primary database model

Document store

Relational DBMS

Vector DBMS

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

Score	13.60
Rank	#39	Overall
	#6	Document stores

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Score	1.53
Rank	#145	Overall
	#7	Vector DBMS

Score	1.48
Rank	#149	Overall
	#8	Vector DBMS

Website

firebase.google.com/products/realtime-database

azure.microsoft.com/services/synapse-analytics

github.com/qdrant/qdrant
qdrant.tech

github.com/weaviate/weaviate
weaviate.io

Technical documentation

firebase.google.com/docs/database

docs.microsoft.com/azure/synapse-analytics

qdrant.tech/documentation

weaviate.io/developers/weaviate

Developer

Google

acquired by Google 2014

Microsoft

Qdrant

Weaviate B.V.

Initial release

2012

2016

2021

2019

Current release

1.19, May 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

commercial license available with Weaviate Enterprise

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.

Implementation language

C++

Rust

Server operating systems

hosted

Docker
Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

yes

Numbers, Strings, Geo, Boolean

yes

string, int, float, geo point, date, cross reference, fuzzy references

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

Keywords, numberic ranges, geo, full-text

yes

all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed

SQL

Support of SQL

yes

GraphQL is used as query language

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

ADO.NET
JDBC
ODBC

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

Java
JavaScript
Objective-C

C#
Java
PHP

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

limited functionality with using 'rules'

Transact SQL

Triggers

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Collection-level replication

yes

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, tunable consistency

Eventual Consistency

Foreign keys

Referential integrity

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

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, based on authentication and database rules

yes

Key-based authentication

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

Weaviate is an open source vector database that is robust, scalable, cloud-native,...
» more

Competitive advantages

Flexible deployment - Free, open source or fully-managed cloud vector database service...
» more

Typical application scenarios

As a database supporting the development of generative AI and semantic search applications...
» more

Key customers

All companies that have data.
» more

Market metrics

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
» more

Licensing and pricing models

Weaviate is open-source, and free to use. Weaviate is also available as a fully managed...
» more

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

Related products and services

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

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Weaviate, an ANN Database with CRUD support
2 February 2021, Etienne Dilocker, SeMI Technologies (sponsor)

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Recent citations in the news

Realtime vs Cloud Firestore: Which Firebase Database to Choose
8 March 2024, Appinventiv

Don't be like these 900+ websites and expose millions of passwords via Firebase
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19 March 2024, BleepingComputer

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14 May 2024, TechCrunch

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31 July 2024, Datanami

StructuredRAG Released by Weaviate: A Comprehensive Benchmark to Evaluate Large Language Models’ Ability to Generate Reliable JSON Outputs for Complex AI Systems
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Build enterprise-ready generative AI solutions with Cohere foundation models in Amazon Bedrock and Weaviate vector database on AWS Marketplace
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