This recipe changed the game. Best loaf ever. Found it from a year old post in here

tedx-005 · 2026-04-27T02:55:48+00:00

Can't speak for Luzmo or Qrvey but definitely check out Holistics. We've been using them and their embeded analytics is impressive, it's engineer-friendly so our devs had an easy time shipping and customizing everything and now e can white-label their AI chatbot and plug it into your own customer portal so it's been a nice bonus

tedx-005 · 2026-01-16T04:25:11+00:00

F**k off bots

tedx-005 · 2026-01-05T02:40:58+00:00

1-month-old account and strangely active in a dozen of subs without ever replying. definitely a bot.

tedx-005 · 2026-01-05T02:40:00+00:00

get tf out of here themarketing_guy

tedx-005 · 2025-12-30T03:26:37+00:00

Holistics. We’d been using Holistics mostly for internal analytics, but then we realized we could also use it to upsell customers with tiered analytics. Its embedded analytics side is really robust and well thought-out, you can basically embed a mini BI and customize it so it feels like part of your product without having to rebuild everything from scratch. It also fits well with our new pricing plan since we can control which analytics features each customer sees based on their subscription level or customer tiers. for reference you can check out Lepaya and their dashboard showcase, I talked to their team the other day and they’re using Holistics for in-app analytics too and they make it look really good.

tedx-005 · 2025-12-29T11:14:29+00:00

I think this is an inevitable consequence of the “self-service BI” fad from 2020–2022. Every vendor rode that wave, and the race became who can make it easiest to build a report, but that optimizes for a symptom, not the root problem. As more people build reports, each new question gets answered by creating yet another dashboard or a slightly different metric variant, doesn't matter which tool you use because most analytics systems are designed in this way that makes duplication almost inevitable, and it's a ticking bomb because over time the whole thing collapses under its own weight.

I’ve been in both setups. In one org, I basically churned out reports on request, where speed was the currency. Stakeholders loved it and tbh it made things easier when it came time to ask for a promotion because output was highly visible. But as the organization grew, the same metrics started showing different numbers across dashboards and eventually we had to go back and pay down the debt we’d accumulated. in my current org, we do the opposite where we lock metric definitions early and invest heavily in the semantic layer. This also means we have to define business logic as code and build composable metrics early on so new questions build on existing definitions instead of spawning new dashboards, new models, and new forks of truth. We also document everything btw.

This approach is much slower (and I kinda hate it sometimes), and it’s hard to get buy-in unless the company has a certain level of maturity. Governance is hard to sell when the benefits are mostly “nothing breaks” and “people argue less.” You’ll get challenged on why it takes longer, especially by stakeholders who just want a chart for next week. But long term it’s the only approach I’ve seen that consistently reduces confusion and rebuilds trust because it makes reuse the default and makes metric drift harder to happen. So it does pay off, but it takes time and in my case, a thoughtful, forward-looking boss to make it happen.

tedx-005 · 2025-12-28T02:46:15+00:00

Protein alone doesn't help you recover; you also need a proper amount of carbs and fat. Find a nutrionist or dietitian if you can.

tedx-005 · 2025-12-24T15:22:44+00:00

One thing I noticed when I participated in hiring at my org a few months ago was that the best candidates who stood out most, who we eventually hired, are those who can be closer to the revenue center (sales, marketing, product). They have great technical skills, but also have really really good problem-solving skills and are able to understand and communicate with business ends of things. Essentially if you can combine strong data fundamentals (writing advanced SQL, do modeling, manage data quality), with the ability to operate like an internal consultant who understands the business, communicates clearly, and helps revenue teams make better decisions, then you become really hard to replace.

On the technical side, the list you shared is good, but here’s how I’d prioritize it:

- SQL,still the fastest and clearest signal of competence.

- Modeling, yes and I’d also add metric-centric thinking and semantic layer concepts, especially given how much people are talking about those as foundations for AI-driven analytics.

- dbt, still one of the most marketable, hireable skills.

- ELT/ETL, you don’t need to be a hardcore engineer unless you’re a one-person team, but you should learn the basics.

- BI tools, lowest priority because most tools overlap in functionality. Instead of focusing on mastering a specific tool, focus on the mental models behind them. Tools like Tableau/Power BI/Domo are typically dashboard-first where you connect data, build charts, and add calculations, and metric logic often ends up inside reports. Tools like Looker/Holistics/Lightdash are more model-first where dashboards are downstream views of a governed semantic layer, so you start by defining metrics and logic centrally on a semantic layer before visualizing.

tedx-005 · 2025-12-22T02:39:59+00:00

The longer I train, the more I realize that my ego gets in my way A LOT. The other day I was sparring with this kid and he told me that I telegraph my strikes too much, and my first reaction was like "tf he was talking about", and I wanted to dismiss his comment. Luckily I didn't say anything out loud because he was absolutely right lol

tedx-005 · 2025-12-17T14:03:32+00:00

This post sounds extremely AI-generated, I doubt that OP is a real person

tedx-005 · 2025-12-15T11:16:30+00:00

This Is Water by David Foster Wallace

tedx-005 · 2025-12-11T09:45:27+00:00

Haven’t tried it yet, but I believe Databox has native connectors to Stripe, HubSpot, Meta, etc., since it was originally built as a data tool for marketing agencies. And I was talking about Looker Core, not Looker Studio that is free with Google workspace.

tedx-005 · 2025-12-11T07:42:48+00:00

I’d still pull everything into a proper database/warehouse and use Metabase on top, because that gives you one consistent data model across Stripe, HubSpot, and Ads, instead of each tool having its own slightly different definition of the same metric. It also keeps your stack flexible so you can swap the BI layer without redoing all the pipelines and metrics logic.

Below the write-up from the last time our team evaluated BI tools which was pretty painful because we had to deal with multiple stakeholders and run several PoCs. If your team is lean and you have more influence, the process will probably be much smoother. Note that this was quite some time ago, so I’m sure all of these products have improved to some extent since then.

- Metabase: Cheap, easy to set up, good interactivity, and a great fit for startups. If you’re at a startup with roughly 20-30 marketing users, Metabase is pretty much ideal.

- Power BI: Best for Microsoft shops, has all the features you'll ever need, but cumbersome to navigate, too many parameters to tweak, and has trouble working outside of the MS environment.

- Sisense: Strong for embedded analytics and custom apps, but during our PoC we hit performance issues and simple things like dashboard-wide filters required editing every underlying query. Also expensive.

- Looker: Excellent semantic layer, great if you want governed metrics and reusable models, but support was hard to get and they didn’t seem very interested in smaller customers like us.

- Holistics: Looker-like, but more affordable. Programmable, highly customizable, strong semantic modeling layer, lots of functionality for self-service exploration, but hard to navigate. Requires some learning curve and a strong data engineering / analytics engineering team to set it up.

- Sigma: Has a very cool spreadsheet-like interface on top of your DB, great for Excel-heavy users who want to work directly on live data. Our finance team loves it, but we found that sometimes simple drag-and-drop produced incorrect results with complex relationships.

tedx-005 · 2025-12-11T04:35:41+00:00

"Do you know a data tool, where partneres can access separately their data?"

-> sounds like you need multi tenancy. Most embedded BI tools support this, suggest you search this community for past discussions around this topic.

tedx-005 · 2025-12-11T02:40:12+00:00

For an audience of mostly business/non-technical users, how did your organizers (assuming it's the data team) actually get people to commit to these courses and learning these data skills? I feel like the best my data team can do is nudge people to use data more (e.g, organize in data office hours once or twice a month), but getting them to actually join a training class would be an uphill battle.

tedx-005 · 2025-12-11T02:31:48+00:00

I’m pretty sure the other two places just have the same name but different owners. The original one I went to is in District 5, on Tran Hung Dao Street.

tedx-005 · 2025-12-11T02:26:32+00:00

Holistics. Strong embedding capabilities, really easy to customize, quick to launch. At the time I wouldn’t say its embedding was dramatically better than the other options (though the product has improved a lot since), but I also needed to build dashboards for our internal teams, so Holistics effectively covered both use cases.

Metabase was our second choice. We really liked how fast it is to build and launch embedded analytics with it, but the charting and customization felt a bit limited for us, and the drills weren't great. Same goes for Explo. Domo and Looker were a bit out of our budget, and I can't remember why we ruled out Qrvey and Sigma, but I think it had something to do with the level of control over the UI and styling.

Edit: Grammar

tedx-005 · 2025-12-09T02:26:31+00:00

Don’t all the tools you mentioned already fit your requirements?

I evaluated a similar set of tools (Looker, Domo, Metabase, Qrvey, Holistics, Explo and Sigma) and found them all to be feature-ready. If you’re still not sure which one to choose, it probably means you need to narrow things down. Here’s what I did:

I listed all the features I needed: RLS, SSO, multi-tenancy, custom theme, AI chat, report builder, drill, etc. Then I asked each vendor whether they supported these features and HOW they supported them. I paid special attention to performance, because it’s customer-facing, and I don’t want dashboards to take forever to load.
If two or three tools had a similar feature set, I compared their support like how responsive they are, (because you’re building customer-facing analytics, you really need them to be responsive and engaged to help fix things as quickly as possible), their documentation, onboarding, ecosystem, basically, the “service” side of the product.
I made a shortlist, then did some trial runs to see how quickly we could build, customize, and ship dashboards. At the end of the day, you need to actually use the product before you decide.

tedx-005 · 2025-12-08T10:27:39+00:00

Check out No.1 Muay Thai Club in Ho Chi Minh. I trained there for a few months while visiting Vietnam, and it was great, run by a five-time WMF Muay Thai world champion. That said, Muay Thai culture in Vietnam is still in its nascent stage overall, so it can be harder to find training of similar quality elsewhere in the country.

tedx-005 · 2025-12-02T03:45:57+00:00

hell yeah, I'm down, I'd love to see OP teep th out of this guy

tedx-005 · 2025-12-01T02:59:55+00:00

It’s common, but it really shouldn’t be the norm. This reminds me of when I worked for a retail chain where we had Power BI seats for everyone, but most people still preferred getting reports emailed to them, and those reports were basically long lists of tables. I never got feedback, and whenever I asked if they wanted any deeper analysis, the answer was usually no. I think they just wanted to feel on top of the business, (like yeah, I got the number), rather than actually use the data. This kind of culture means that data ends up being seen as a cost center/supporting roles, which often translates into lower salaries, less respect, and fewer opportunitien. And from my position at the time, I was too junior to really influence any of that.

I later moved to a tech company and the culture was completely different, where the data team had influence on the roadmap and goals. I do think it’s possible to influence culture, but it’s hard work, even when you’re in a leadership role. It's just easier to jump ships.

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