Count is a new type of data analytics application, where everything is based around notebooks.
Notebooks contain all of your analytics queries, alongside rich text, images, videos, and interactive controls. A notebook can be a simple static document, a fully interactive application, or anything in-between. They are backed up as you write, use state-of-the-art rendering technology to take full advantage of your machine, and scale down to stay readable on mobile.
Count connects to your data warehouse to run queries, so the data you see is always up-to-date. It also (optionally) intelligently caches results to minimise the load on your databases.
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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Qrvey's answer:
Product Leaders that include Product Management and Engineering Teams and CEO/CTO/CPOs of B2B SaaS Companies
Qrvey's answer:
Customers choose Qrvey for the following reasons:
Qrvey's answer:
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, Count.co should be more popular than Qrvey. It has been mentiond 5 times since March 2021. 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.
Hi Reddit, after lurking here a while I've finally got something interesting to share - a new feature I've been working on at Count (https://count.co), which I wrote a blog post on here:. Source: 5 months ago
Hi HN, after seeing a lot of data engineering discussion here I thought it would be interesting to share a new feature I've been working on at Count (https://count.co). We've made possible to import and execute dbt models, compiling them on the fly with a custom compiler, and view results alongside other collaborators in real time. We built this because we heard feedback from our customers that debugging and... - Source: Hacker News / 5 months ago
Count | Senior Software Engineer | REMOTE within UK/Europe | Full-time | https://count.co Count is like Jupyter, Tableau and Miro combined in one tool. Data teams at some of the world's leading scale-ups use it for everything from iterating data models to performing deep dive analyses and telling impactful stories backed by data. We're a small team of 8, and we're looking for experienced software engineers who are... - Source: Hacker News / 6 months ago
Full disclosure: I do work for count.co, the canvas in which the guide was built. Source: 10 months ago
Nice article! When we wrote the instanced WebGL line renderer for https://count.co one of the tricky parts was switching between mitre and bevel joins based on the join angle - for very acute angles the mitre join shoots off to infinity. Another nice extension (that we are yet to implement) is anti-aliasing, but I think that requires extra geometry to vary the opacity over. - Source: Hacker News / almost 3 years ago
Since you're on AWS already, check out https://qrvey.com. Source: 5 months ago
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