
Zerve AI
Quadratic
DataLab
Hex
Metabase
Saturn Cloud
Observable
Data notebook built for speed, visibility, and collaboration

Metaplane
Know Your Data
Flatlogic
Qualdo™
Motion Project
Instadeq
DataQuest Beta
Percival is a declarative data query and visualization language.

Which is more popular?
Based on our record, Percival seems to be more popular. It has been mentioned 3 times since March 2021.
Website, pricing, platforms and company facts side by side.
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| Website | hyperquery.ai | percival.ink |
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What each product offers, as listed by its team.


No features have been listed yet.
Possible disadvantages
Walkthroughs and reviews on video.
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COMPLIMENTED HYPE BEAST | PARFUMS DE MARLY PERCIVAL FRAGRANCE REVIEW
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How often each product is chosen within a category, 0–100% relative to the other.


Share your experience with using Hyperquery and Percival. For example, how are they different and which one is better?
Recommendations tracked on public social media and blogs since March 2021.


Tracking Hyperquery since Feb 2023.
I struggle to understand the Clojure/Datomic dialect, but I agree generally. I recommend Percival for playing around with Datalog in a friendly notebook environment online: https://percival.ink/ Although there’s no “ANSI SQL” equivalent... - Source: Hacker News / over 1 year ago
You could theoretically use Datalog anywhere you're currently using a SELECT SQL statement - it's a declarative way to produce a set of tuples given some existing sets of tuples. But, Datalog is much better at deductive reasoning... - Source: Hacker News / almost 4 years ago
I haven't written a parser with Chumsky, bit I've played with a little one a bit if you wanna see an example syntax. The error reporting for this project is implemented with `ariadne` which is also really slick. Parser:... - Source: Hacker News / about 4 years ago
When comparing Hyperquery and Percival, you can also consider the following products.


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Infinite canvas spreadsheet for data science with Python, SQL, and formulas.
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Understand datasets & improve data quality, by Google PAIR
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