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Monday, July 22, 2024

Unleashing Streamlit’s Energy: Constructing Characteristic-Wealthy Knowledge Purposes With Headless BI

Not too long ago I wrote an unconventional article about exposing analytics use circumstances in digital actuality. Although it was only a hackathon undertaking, it pushed me to consider what APIs (and during which kind) must be uncovered by headless BI platforms.

After we speak about front-end improvement, we normally speak about Javascript/Typescript libraries. This was the case with the VR demo talked about above. However, particularly within the case of knowledge (analytics), Python language grew to become extraordinarily well-liked not solely on the again finish but additionally on the entrance finish. Probably the most well-liked ecosystems these days is Streamlit.

An thought popped into my head: create a knowledge software using a full set of APIs, which must be supplied by headless BI platforms.

Presently, one of the vital feature-rich information functions is the one permitting customers to construct reviews (visualizations/charts/insights), so I made a decision to create such an software utilizing Streamlit and our Python SDK.

This text is backed by an open-sourced demo. It accommodates not solely the Streamlit app but additionally a corresponding end-to-end information pipeline. It’s value mentioning that the demo means that you can create a single pull request to ship the whole lot constantly:

  • Extract from information sources and cargo to the info warehouse (Meltano)
  • Knowledge transformations (dbt fashions)
  • Declarative definitions of analytics (GoodData)
  • Knowledge functions (VR demo, Streamlit)

Why Headless BI?

We describe it right here.

Particularly, you possibly can join Streamlit on to information warehouses and even to recordsdata, however headless BI gives extra:

  • Declare a semantic mannequin simply as soon as (logical information mannequin, metrics, reviews, …)
  • Join any shoppers (together with Streamlit), whereas counting on a single supply of fact
  • Present low-enough latency to finish customers (scalability, caching)
  • Forestall information warehouses from changing into efficiency bottle-necks or being too expensive


Let me spoil it right here and present you the complete image first. It is a screenshot of the ultimate software:

What are you able to see within the image? What am I going to speak about within the following chapters?

Use circumstances in self-service analytics!


  • Semantic mode — offered within the left panel. Customers construct reviews by choosing enterprise names. No SQL!
  • Reviews: offered in the primary canvas. Varied visualization sorts.
  • Interactivity: filters, sorting
  • Context consciousness – catalog is filtered primarily based on an already present report
  • Multi-tenancy – change between a number of remoted workspaces
  • Caching – each Streamlit and GoodData caching

If you wish to begin instantly with a hands-on expertise as an alternative of making ready the entire ecosystem in your laptop computer, you possibly can strive it right here.

In any other case, begin with the top-level README to organize information and analytics, then observe it with the README for the Streamlit app to start out the app regionally.

Semantic mannequin

The demo repository accommodates all of the details about how the semantic mannequin is generated.

We wish to expose the mannequin to finish customers within the Streamlit information software. Python SDK supplies numerous features for this objective. It’s doable to listing every kind of entity – e.g. listing attributes, information, metrics, and so on. Moreover, it supplies a operate to return the complete catalog.

Furthermore, the SDK supplies a operate to filter the mannequin by the already present report. What does it imply? If you put some entities right into a report, it could restrict what different entities you possibly can mix them with. The mannequin consists of datasets linked by relations. Not all datasets should be linked, and even when they’re, the route of the connection can impression the flexibility to mix the entities.

Lastly, we wish to cache the catalog so we don’t name the backend with each web page refresh.

As an example, right here is the operate amassing the entire semantic mannequin (catalog):

Then, a Streamlit element like “multiselect” may be populated by catalog entities:

Helper features are used right here to extract IDs and titles. Additionally, the Streamlit state is utilized right here to set the chosen values.

Report executions

Python SDK supplies numerous choices on how one can execute reviews. As a result of we’re constructing a Python software, it is sensible to make use of the Pandas extension, which might return Pandas information frames. They are often printed 1:1 in Streamlit or they are often immediately handed as arguments to varied visualization libraries supplied by Streamlit, on this case, I exploit the Altair and Folium libraries.

We have to gather all the chosen catalog entities and fill them right into a report definition.