Using Dashboard Filtering to Get Customer Usage in Seconds from TBs of Data

Authored by Conall Heffernan

As the Customer Success lead at Bronto, I need fast, reliable insights into customer health and product usage — but I don't have time to constantly update indexes, schemas, or individual widgets just to answer new questions. I need to spot patterns, explore trends, and get answers in real time without manual overhead.

That's why Bronto's dashboards are so critical to my work. We recently added a new query filtering feature that lets me use SQL to look for any pattern across all widgets in a dashboard simultaneously — and update everything at lightning speed.

What Bronto Dashboards Provide

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  Rich visualisation options — time-series charts, geomaps, numeric value widgets (with units like bytes and time), top lists, treemaps, and log event lists for drilling into raw data


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  AI widget builder — describe what you want in natural language; an LLM builds the query and creates the widget without you needing to know the query language or which datasets to select (
  
  
  
  
  
  

  
  
  
  The Power of Filtering at Scale
  

  
  
  

  In many logging or observability tools, applying a filter means updating every single chart, table, or widget individually. Tedious. Time-consuming. It doesn't scale.

  
  
  

  With Bronto, applying a filter in the main query bar instantly updates every single widget on the dashboard for your given timeframe. With a default retention period of one year, you don't need to worry about missing long-term trends — all your data is fully searchable and visualisable. Concerned about when an issue started? You can analyze trends over months, not days.

  
  
  

  Our widgets use pre-computed log-based metrics (LBMs) for rapid responses, but the new dashboard filtering goes further — running raw log queries to filter the data in your dashboard in real time. Results come back in seconds, and you can drill down across all widgets simultaneously using SQL or by clicking from a dropdown of top keys and values.

  
  
  

  One thing worth noting: there's no initial configuration of keys required. I can use any key I want in the filter, with no setup.

  
  
  
  
  
  
  
  

  
  
  
  How I Use Dashboard Filtering for Customer Usage
  

  
  
  

  My primary use for this feature is gathering and presenting product usage data to our leadership team. Questions like:

  
  
  
  
  
  • "How much data did Org ID 54321 send over the last 6 months?"
  
  
  • "How much did company ACME search last month?"
  
  
  
  
  

  Instead of building 10 custom dashboards (which doesn't scale as your customer base grows), I use dashboard filtering:

  
  
  
  
  
  1. Navigate to our main Usage Dashboard
  
  
  2. Enter the specific org_id in the main query filter (e.g. org_id: 54321)
  
  
  3. Every widget updates instantly to reflect only that organization's data
  
  
  
  
  

  That's it. A complex, multi-step data lookup becomes a quick and easy process.

  
  
  

  The first time I tried filtering across the dashboard for an org_id, I thought something wasn't working right — the results were rendered so fast across terabytes of data. It was a genuine "wow" moment. As a customer support lead, it's great to see the under-the-hood changes we're building for customers also improving my own day-to-day.

  
  
  

  
  

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