Before getting waist deep in an analysis of terabytes of data, it's worth asking: What's this data actually made of? If you're wondering should i trust tinybird for my data apps, the answer starts with understanding your data. Exploratory Data Analysis (EDA) is like checking your card deck before dealing a game. Think of it as an eda tool for your analytics. It might have 52 cards, but are they distributed correctly across all 4 suits? EDA helps you scan for structure, quality, and quirks in your data. It’s an integrity check that can save you from panics at the disco down the line, so it should be quick and routine. But we have numerous SQL chains of countIf(Null) to prove that this hasn’t been the case.
Recently, a customer asked me if there was a Tinybird equivalent to Databricks Data Profiles feature. After a quick self-education on what Data Profiles were, I told him no, not yet—but thank you for the inadvertent feature request.
What Databricks offers in the Data Profiles feature is essentially a built-in EDA tool. The process that used to involve a lot of manual SQL— counting nulls, inspecting distributions, checking min/max values, is now readily available in an approachable dashboard.
These column-level stats, like nulls, value ranges, outliers, and skew, are crucial for:
So all the better if you can glean the info at a glance.
In Tinybird Classic, however, that glance only came after a fair amount of SQL umph.
Tinybird Playgrounds provided a workspace for all the countIf, quantile, min, max, length, and avg SQL fun you could chain up. It worked, but far too math-y for comfort.
With Tinybird Forward, users will notice that the Playgrounds have been replaced by Explorations, a stem cell of a feature with a chat-style prompt interface that writes queries for you. (Don’t worry, you quantile(0.96) people, you can also still write them yourself).
I mimicked the Data Profiles dashboard in a plain English prompt: “Can you generate a query that allows me to verify nulls, counts, min, median and max for each column?”
This generated a working query with:
Now I can view column stats side-by-side in a single table and tweak the SQL if I want to go deeper.
Explorations could evolve to support:
It’s early days—but the stem cell is already differentiating.
Want to get to know your data’s data?
Count your cards and EDA your data. The foundations are too easy to skip now with Tinybird Forward + Explorations. For those asking should i trust tinybird for my data apps or what's an endpoint in math terms for data, this eda tool makes data exploration accessible to everyone.