Qrvey
DevicePilot
AnswerRocket
Syndigo
Amdocs CES
Yellowfin
Omniscope
Toucan Toco
Socket.io
Firebase
Pusher
SignalR
PubNub
Histats
StatCounter
AFSAnalytics
Designed for external use cases where SaaS companies need to provide their customers with powerful and customizable analytics capabilities.
Qrvey is the only full stack solution that offers all the embedded visualization and self-service analytics tools along with a unified data pipeline that offers a data lake optimized for multi-tenant analytics.
Qrvey's embedded visualizations empower engineering teams to build custom experiences, along with full white labeling and CSS customization options to make Qrvey’s javascript widgets blend seamlessly into a SaaS application. ⋅⋅* Qrvey’s data-driven automation workflows enable the creation of complex workflows based on data triggers, such as conditional logic, nested functions, data write-backs with notification integrations to third party systems such as Slack. ⋅⋅* Qrvey supports natural language querying of data using generative AI to easily spot trends and outliers, augmented analysis capabilities. ⋅⋅* Qrvey also supports pixel perfect reporting to generate printable reports from the same analytics data.
Qrvey simplifies data management by providing a single data pipeline solution featuring a data lake solution that is optimized for multi-tenant analytics. This contains native data connectors and APIs to ingest data in any type from any source, including real-time data with live connections. ⋅⋅* Qrvey’s semantic layer can inherit and map security models from your multi-tenant SaaS application, saving software development teams the hassle of duplicating users and roles. ⋅⋅* Qrvey’s robust API allows you to create data delivery services and managed download functions that go beyond basic exporting.
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Product Leaders that include Product Management and Engineering Teams and CEO/CTO/CPOs of B2B SaaS Companies
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Customers choose Qrvey for the following reasons:
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Qrvey's approach to embedded analytics is different. Qrvey combines the best of BI, data warehousing, and data visualization into a single solution built exclusively for SaaS applications.
Qrvey's key features include:
100% Embeddability - Everything is embeddable with JS based components that supports full white labeling so you can create unique analytics experiences within your SaaS application.
Data Warehouse included - Visualizations are useless without a scalable data layer built specifically for analytics use cases. Qrvey includes native multi-tenant support so your data is ready for your multi-tenant SaaS application. This includes data syncing and API support that allows for any type of data to be ingested into the Qrvey data layer.
Self-Hosted - Deployed to Your AWS Environment. Customers get ultimate control as Qrvey is deployed to their AWS environment inheriting and respecting their security policies. Your data never leaves, but it's ready for analytics now.
Based on our record, Socket.io seems to be a lot more popular than Qrvey. While we know about 718 links to Socket.io, we've tracked only 1 mention of Qrvey. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Since you're on AWS already, check out https://qrvey.com. Source: 6 months ago
Previously we created a chat with pusher. But this time we are going to do it with Socket.io. Socket.io is a NodeJS library. With it we can create our own servers. This is cheaper than using pusher server and we have more control on the code. - Source: dev.to / 9 days ago
The first is the script tag in the head of our HTML document that loads the Socket.IO client library. This script tag includes the Socket.IO client library that will communicate with our socket.io server from the code above. - Source: dev.to / 27 days ago
Before diving into this tutorial, if you find microservices mysterious, check out my previous article for a detailed explanation. In this hands-on tutorial, we'll build a real-time chat server using Node.js, Socket.io, RabbitMQ, and Docker. Get ready for a practical journey into the world of microservices! Let's begin. - Source: dev.to / 3 months ago
Now we will be implementing socket logic using socket.io for building websockets. This library provides an abstraction layer on top of WebSockets, simplifying the process of creating real-time applications. For better maintainability, it is recommended to create a separate file for socket calls. To do this, navigate to the src folder, create a folder named services, and inside it, create a file named socket.ts... - Source: dev.to / 4 months ago
Hi I made a chat app using socket.io it worked fine locally but when I deployed the app on render.com socket is not working properly I have to refresh the page to see new messages please help... Source: 5 months ago
DevicePilot - DevicePilot is a universal cloud-based software service allowing you to easily locate, monitor and manage your connected devices at scale.
Firebase - Firebase is a cloud service designed to power real-time, collaborative applications for mobile and web.
AnswerRocket - AnswerRocket is a search-powered analytics that makes it possible to get answers from business data by asking natural language questions.
Pusher - Pusher is a hosted API for quickly, easily and securely adding scalable realtime functionality via WebSockets to web and mobile apps.
Syndigo - Syndigo is an online management platform that provides access to the world’s biggest global content database of digital information.
SignalR - SignalR is a server-side software system designed for writing scalable Internet applications, notably web servers.