Qrvey
DevicePilot
AnswerRocket
Syndigo
Amdocs CES
Yellowfin
Omniscope
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Observable
RunKit
D3.js
Jupyter
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nbviewer.org
Vega-Lite
Microsoft Power BI
Qrvey is the only solution for embedded analytics with a built-in data lake. Qrvey saves engineering teams time and money with a turnkey solution connecting your data warehouse to your SaaS application.
Qrvey’s full-stack solution includes the necessary components so that your engineering team can build less.
Qrvey’s multi-tenant data lake includes:
Qrvey’s embedded visualizations support everything from: - Standard dashboards and templates - Self-service reporting - User-level personalization - Individual dataset creation - Data-driven workflow automation
Qrvey delivers this as a self-hosted package for cloud environments. This offers the best security as your data never leaves your environment while offering a better analytics experience to users.
The result: Less time and money on analytics.
Qrvey ObservableNo features have been listed yet.
Qrvey's answer
Product Leaders that include Product Management and Engineering Teams and CEO/CTO/CPOs of B2B SaaS Companies
Qrvey's answer
Qrvey takes a different approach to embedded analytics. Instead of focusing almost completely on the front end, we know that any analytics function starts with data.
Qrvey includes a full-featured data lake powered by Elasticsearch, not a basic relational caching layer. Furthermore, by including a data lake, the cost to scale out is much less than traditional data warehouses.
For the user-facing components of the platform, Qrvey offers more embedded components and APIs to personalize the experience beyond static dashboards. Qrvey offers:
All of this is backed by a semantic layer that makes integrating Qrvey into the security model of SaaS applications simple.
Qrvey's answer
Customers choose Qrvey for the following reasons:
Based on our record, Observable seems to be a lot more popular than Qrvey. While we know about 287 links to Observable, 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
Curious to see whether more recent dithering approaches would produce better results. They don't even have to be more resource hungry than the classic Bayer or Floyd-Steinberg dithers! Interleaved Gradient Noise[0][1][2] comes to mind as an alternative to Bayer, and it can even be approximated quite well with just 8-bit operations! Basically, use the following function to determine your threshold based on pixel... - Source: Hacker News / about 13 hours ago
Could this be implemented in Rust? Does that project (sqlite-loadable-rs) support WASM? https://observablehq.com/@asg017/introducing-sqlite-loadable-rs. - Source: Hacker News / 19 days ago
Have you tried out a tangled-tree visualization? [1] I've found it to be super useful when visualizing these sorts of relationships in a compact way. [1] https://observablehq.com/@nitaku/tangled-tree-visualization-ii. - Source: Hacker News / 19 days ago
Maybe I'm easy to impress, but I always stop and play around with the nested tree example when I come across Sortable. It works so flawlessly, and feels very tuned to mobile dnd. It even works to arrange (and reflow) inline spans in a paragraph! I have yet to come across this functionality in a text editor.. [0]: https://observablehq.com/@dleeftink/sortable-playground. - Source: Hacker News / 27 days ago
Arrow JS is just ArrayBuffers underneath. You do want to amortize some operations to avoid unnecessary conversions. I.e. Arrow JS stores strings as UTF-8, but native JS strings are UTF-16 I believe. Arrow is especially powerful across the WASM <--> JS boundary! In fact, I wrote a library to interpret Arrow from Wasm memory into JS without any copies [0]. (Motivating blog post [1]) [0]:... - Source: Hacker News / 29 days ago
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