Hyperquery

Data notebook built for speed, visibility, and collaboration

4.3•7 reviews•

1.1K followers

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Hyperquery is the data notebook for teams that enables you to easily build shareable analyses (in SQL or Python).
Hyperquery gallery image
Hyperquery gallery image
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Launch tags:Analytics•Data & Analytics•Data Science
Launch Team
Migma AI
Migma AI
Lovable for Email
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What do you think? …

Robert Yi
Maker
📌
Hey ProductHunt! I’m Robert, CPO and Co-founder of Hyperquery. 👋 We are so excited to finally announce Hyperquery is in public beta! Hyperquery is a data notebook for teams. But unlike technical tools of the past, we’re built from the ground up for analytics. We’re faster and more intuitive to use, more easily organized, and more collaborative than your SQL IDE or Jupyter notebook (yet even more powerful). - Do your work in SQL or Python. - Write it up easily with our WYSIWYG (what you see is what you get) editor. - Share and organize it with our intuitive file structure. But our vision is much broader than that - we don’t want to just add another technical tool to the analytics arsenal, but make analytics better. Existing tools create knowledge silos and reinforce our status as SQL monkeys. The effect? Poor understanding of our value, job dissatisfaction, and overall a sense of utter chaos. Hyperquery is intended to make it not only easy to build analyses, but make them centralized and collaborative, so the chaos is reigned in, conversations and engagement are elevated, and you have a space deserving of your value add. We’d love to hear your thoughts & feedback so drop some comments below! 👇 We’ll be here all day to answer your questions!
Eddie Forson
Congrats on the launch! This looks really great. I'm very intrigued with Hyperquery. It seems like Notion + Jupyter notebooks had a child 😆. The tool looks sleek. What type of companies and what persona that you are targeting with your product?
Luong Vo Tran Thanh
Maker
@ed_forson Wow, thanks for the commend, Eddie !! Notion is a popular product. Jupyter is also what our current customers referred to when they first experience our Code Blocks. It is amazing to live in a world with diverse softwares, most of which rejoin at some intersections for a magical and excellent customer experience. We are happy you are finding these intersections from Hyperquery. As for our current target customers, we craft the product for analysts and data scientists. It would also be rewarding for teams and companies that want to get the best out of data with minimal technical maintenance effort and a low learning curve. That being said, we continuosly learn from customer feedbacks and potential prospects so our product is always open to every team and evolving for better offerings
Rachel Seo
Maker
Thank you @ed_forson!! Yes to add to what @luongvo209 said...our power users have been analysts and data scientists who had been looking for a shared analytics space to work together as a team and with their stakeholders. 🙂
Robert Yi
Maker
@ed_forson Really appreciate that! We clearly drew a bit of inspiration from Notion when figuring out how to organize our notebooks. 😅 As @luongvo209 mentioned, most of our users are analysts and data scientists doing heavy deep-dive analytics work. 😁 We do have a number of data eng using us as well, though.
Kevin Kong
Maker
"Notion + Jupyter notebooks" just made my day! Thank you for the support @ed_forson :) Our users' personas run the gamut, but our power users tend to be analysts & data scientists & their stakeholders. Hope to see you try out the product soon!
Damian Rodriguez
Maker
Thanks for the shoutout, @ed_forson! We're glad you find our tool intriguing and think it's a combination of Notion and Jupyter notebooks. Our target audience consists of data analysts, scientists and their stakeholders who are looking for a shared analytics platform to work together effectively. We appreciate your support and hope you'll give Hyperquery a try soon!
Laurie Hérault
Luong Vo Tran Thanh
Maker
@laurieherault we are pleased to hear that your first aesthetic impression was positive. Notion is a great product. In fact, we'd love to share the audience and together improve your workflow, may it be data analysis or general purposes. You might want to check out one of our feature, embeds in Notion / Confluence
Robert Yi
Maker
@laurieherault @luongvo209 Appreciate it @laureherault! Our brand designer @daoun_jeong built the whole thing from scratch (she's amazing!).
Damian Rodriguez
Maker
Thank you for your kind words about our homepage @laurieherault! Our goal was to make data analysis as accessible and easy-to-use as possible, and we're thrilled to hear that you think we're on the right track. If you're interested, you should definitely check out our feature for embedding analysis directly into Notion and Confluence as @luongvo209 mentioned!
Joseph Moon
Maker
Kevin Kong
Maker
@laurieherault glad to hear that feedback! Notion upped the ante for all B2B SaaS companies' to elevate their product experience. As much as Notion made documentation actually FUN for all companies, we want to make data notebooks delightful and collaborative for data teams!
Vu Pham
Hyperquery rocks! I can finally say goodbye to those boring reports filled with static images and scattered links. With Hyperquery, I can present my findings in a cool, all-in-one package. And the best part? It makes collaborating and fixing issues with my team a breeze. Can't wait to see more features from the team!
Luong Vo Tran Thanh
Maker
@phamhoaivu911 Thank you Vu!!! As one of the makers I am genuinely impressed by your reference as an "all-in-one package". Let's get your team onboard soon and we can share all your great "findings"
Robert Yi
Maker
@phamhoaivu911 @luongvo209 Heck yes! Thanks for that @phamhoaivu911!!!
Damian Rodriguez
Maker
Wow, glad to hear you're enjoying Hyperquery, @phamhoaivu911! Collaboration and presentation of findings are two key features of our product, and it sounds like it's really making a difference for you. We're always working on new features and improvements, so keep an eye out for what's next!
Joseph Moon
Maker
@phamhoaivu911 All-in-one is what we're going for!
Kevin Kong
Maker
@phamhoaivu911 Wonderful summary of our value-add! Thank you for your support :)
Lap Phan
Intriguing product. How much of a learning curve is it for adopters? And who would be the target users vs customers?
Robert Yi
Maker
@lnp Great question! We have an extremely low learning curve, unlike traditional analytics tools like IDEs or Jupyter. 🙂 Everything is accessible through a simple slash menu (/query for query blocks, /python for python blocks). Target customers are typically analysts and data scientists, though we do have quite a few data engineers using us as well. 🙂
Rachel Seo
Maker
Hi @lnp! @rsyi said it perfectly but to add: we like to say we're low floor high ceiling. 🙂 Easy to learn for early analysts and data scientists, but also flexible and powerful for the most technical users.
Kevin Kong
Maker
Hi @lnp, I'll add one more! As you might be able to tell, our product looks more sleek and Notion-esque than your clunky Jupyter notebook or warehouse IDE, and this is an intentional design decision to make our product the best choice for BOTH the non-technical stakeholders to consume notebooks, AND the most technical data power users to fulfill their jobs-to-be-done. We've mostly seen analytics teams, data science teams, as well as their stakeholders become champions of the product!
Damian Rodriguez
Maker
Thanks for your interest, @lnp! Our product has a low learning curve, making it accessible and easy to use even for non-technical stakeholders. The interface is designed to be simple and intuitive, with everything accessible through a simple slash menu. The target users of our product are typically data analysts, scientists and their stakeholders, but it's also flexible enough to be used by data engineers. Our goal is to make Hyperquery the best choice for both technical and non-technical users, with a sleek and user-friendly interface that's both easy to learn and powerful to use!
Joseph Moon
Maker
@lnp If you know SQL, it's a breeze to get set up.
Trevor Noon
Love Hyperquery! I've found it so much easier for sharing analyses and reports with teammates as opposed to the raw Jupyter notebooks or any of the other things I've hacked together over the years!
Luong Vo Tran Thanh
Maker
@trevor_noon big thanks, Trevor !! Oh and we look forward to importing from sources like Jupyter Notebooks as well 🙌
Rachel Seo
Maker
@trevor_noon Yes!! Glad to hear that. Thank you for your feedback!
Robert Yi
Maker
@trevor_noon @ryuli 🙌🙌🙌 yes!!! Sharing Jupyter notebooks will soon be a thing of the past. 😁
Kevin Kong
Maker
@trevor_noon you nailed it! Making dinosaurs out of Jupyter notebooks is our goal 😉 Comments, mentions, slack notifications, comments on charts, version history, version control, AI-enabled code-writing, self-documenting warehouses for onboarding, etc. we're incredibly excited to share this future with our community!
Damian Rodriguez
Maker
Thanks for the support, @trevor_noon! We're glad you're finding Hyperquery to be a better option for sharing analyses and reports with your team. We appreciate your feedback and look forward to continuing to improve our product to make it even easier and more convenient for you. Thanks for being a part of our community!
Stedman Blake Hood
Jib
Looks amazing! Do you guys support BigQuery?
Rachel Seo
Maker
@stedmanblake Yes absolutely! You can connect your BigQuery instance and start querying immediately.
Stedman Blake Hood
Jib
Epic - looking forward to giving it a spin! Oh also, have y'all seen Observable? https://observablehq.com/ we're using that now, and I'm curious if you have thoughts on which use cases make the most sense for each
Kevin Kong
Maker
@stedmanblake Yes BQ is one of our most commonly connected data sources, and we use HQ with BQ internally too :)
Joseph Moon
Maker
@stedmanblake Observable is great if you want to make complex visualizations in D3/javascript. We're great for analytics and collaboration in SQL and Python, as we are purpose built for the analytics job-to-be-done! Multiplayer, commenting, linking, mentioning, organization are all much more seamless. Once an organization adopts HQ, the stakeholders of the organization can't have enough of it. One of largest customers has hundreds of stakeholders collaborating on work every week!
Kevin Kong
Maker
@stedmanblake great question! While you could build similarly powerful stuff on either platform, the biggest difference has to be that we target the pool of power SQL + Python users, while Observable has been more friendly to those who come from the JavaScript world :)
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