Tableau Prep Builder: Aggregations (Part 5 of 7)
In Tableau Prep you have to describe your aggregations as a way of reshaping the data, and if you forget to bring a field in, that level just won't come through.
- Unlike Tableau Desktop where aggregations happen in the view, Prep requires a dedicated aggregation step because you're reshaping row-level data into rolled-up output.
- The aggregation step uses two panes: group-by fields on the left (dimensions) and the fields being aggregated on the right (numerical values).
- Aggregating only returns the rows and columns you bring in, so to reconnect aggregated values to the original data you need a join or VLOOKUP.
- Dropping a dimensional field onto the right pane lets Prep auto-detect it and offer count, count distinct, min, max, percentile, or group-by options.
- Cleansing changes like excluding values or renaming fields are captured within the same aggregation step and surfaced in the changes column.
Aggregating in Tableau Prep is different from Tableau Desktop: instead of aggregating inside the view, you add a dedicated aggregation step that reshapes row-level data into rolled-up output, and understanding how that reshaping works stops you losing data or columns you didn't mean to drop.
Using the Sample Superstore flow, Tim strips a flow back to just the clean step, then builds an aggregation step from scratch to show how grouping and aggregating fields works.
- Why Prep needs a separate aggregation step 1:01
In Desktop, aggregation happens live in the view alongside row-level calculations. In Prep you're working with raw row-level data, so you must explicitly describe how to roll rows up — hence a dedicated aggregation step rather than a calculation window.
- Add the aggregation step 1:40
Click the plus icon and choose the aggregate step (shown with a sigma-style icon). Unlike other steps, it opens blank with no summary view, because Prep doesn't yet know what you want to aggregate until you tell it.
- Describe the aggregation with two panes 2:21
Drag grouping fields (dimensions) onto the left pane and the values you want summarised onto the right pane. The summary view updates as you add fields — each additional group-by field breaks the aggregate down into more combinations.
- Cleansing changes live in the same step 3:40
Actions like excluding values or renaming fields taken while in the aggregation step get captured there too, and show up as extra icons in the changes column — Prep stays visual about what's been altered at each step.
- Aggregation drops anything you don't bring in 4:21
Only the rows and columns you explicitly drag into the aggregation step survive into the output; everything else is lost. If you need the aggregated values linked back to the original detail, you'll need a join or VLOOKUP afterwards.
- Aggregating dimensional (non-numeric) fields 5:00
Drag a dimensional attribute onto the right-hand pane instead of a numeric one and Prep auto-detects it, offering count, count distinct, min, max, percentile or group-by rather than sum/average. Choosing group-by moves the field over to the left pane and treats it as a grouping dimension instead.
- Practical use: totals for later calculations 6:34
A common reason to aggregate is to calculate a total (e.g. sales for a whole country) that you then join or VLOOKUP back onto the original dataset, so you can build calculations like a region's percentage contribution to the total.
- Forgetting a field changes the aggregation level 7:20
If you need output at a particular level of detail (e.g. by product) and forget to drag that field into the step, the aggregation simply won't be calculated at that level — always check which fields you've brought in before relying on the result.
- Aggregating is a reshaping operation: it doesn't just summarise a copy of your data, it replaces it, so downstream steps only see the aggregated rows and columns.
- To reconnect an aggregated summary (like a total) back to detailed data, you need a join or VLOOKUP — Prep won't do this automatically.
- The available aggregation options for a field (sum, count, count distinct, min, max, percentile, group-by) depend on whether Prep detects it as numeric or dimensional.
Reach for this when you need summarised or rolled-up values — like category totals or a count of distinct regions — either as a final output or as a value you'll join back onto your original dataset for further calculation.
How this Rollup was made provenance & method
A Rollup is drafted by AI from the video's transcript, then reviewed and edited by Tim. Everything used to produce this one is listed below — the model, the exact prompt, and the source video — so the process is transparent and reproducible.
- Transcription
- On-device — NVIDIA Parakeet v3 for recent videos, OpenAI Whisper large-v3 for earlier ones. The transcript never leaves the machine or gets published.
- Drafting
- Claude Sonnet 5 in the cloud, from that transcript.
- Prompt
- The exact Rollup prompt (v2) — the full system prompt, unedited.
- Source video
- Watch on YouTube
- Drafted
- 5 July 2026 at 09:38
- Reviewed & edited
- 5 July 2026 at 09:41 · by Tim Ngwena
Model + prompt + video is everything you'd need to recreate a Rollup like this yourself. The one thing we don't share is the transcript.
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