CRMA Academy

Combine Datasets into a Final Dataset

Use a recipe to join your Salesforce opportunities dataset with the external CSV amounts into one final, enriched dataset — the everyday move that blends internal and external data for dashboards and stories.

Combine Datasets into a Final Dataset

You've now built the same opportunities dataset two ways, and you uploaded an external CSV earlier. Time for the move you'll make constantly on real projects: blend two datasets into one — Salesforce data enriched with external data — and output a final, dashboard-ready dataset.

This is where the "baseline vs final dataset" distinction pays off. Your opportunities dataset and your CSV are baseline building blocks. Now we combine them into a final dataset a dashboard actually reads.

Set up the recipe

  1. 1

    New recipe → Select Data

    From Data Flows & Recipes → Recipes, start a new recipe. Instead of sync objects, this time select two datasets: your opportunities dataset and the external CSV amounts dataset.

  2. 2

    Keep the Salesforce dataset as the base

    The opportunities dataset holds the lower grain — one row per opportunity. That's your anchor, exactly like Opportunity was in the last lesson.

  3. 3

    Join the external CSV onto it

    Drag the external amounts dataset onto the opportunities node and join. Choose the match — a lookup (or a left join) so every opportunity keeps its row and picks up its external amount.

You don't always need a brand-new recipe. If you already have a recipe producing the opportunities dataset, you can often add another node onto it instead of maintaining a separate recipe. As you scale, actively look for ways to reduce the number of recipes you keep alive — fewer moving parts, fewer things to schedule and debug.

Output the final dataset

Same rule as before: the joins do nothing until you add an output node.

  • Click the plus after the join and add an Output node — this is the finished dataset (e.g. Opportunities with External Amount).
  • Apply, then Save and Run. The recipe executes and materializes the combined dataset.

That's it. You've joined internal Salesforce data with external CSV data into one enriched dataset. From here you could layer on transformations — computed columns, aggregations, filters — but even this simple blend is enough to power a dashboard or a story.

Baseline datasets

The raw building blocks — your synced Salesforce objects joined into an opportunities dataset, and the uploaded CSV. Refreshed by sync + recipe.

Final dataset

The combined, enriched result your dashboards and stories read from. Rebuilt by the recipe whenever its sources refresh.

Keep the order of operations in mind for scheduling: sync refreshes the base objects → the baseline recipe/dataflow rebuilds the opportunities dataset → this combining recipe rebuilds the final dataset. Schedule them in that sequence, or your "final" dataset will quietly serve yesterday's data.

What you can do now

You can create datasets three ways — CSV, Dataset Builder, and recipes — and combine them into finished, analytics-ready datasets. That's the foundation everything else in CRM Analytics stands on.

Next: rehearse the interview questions

Lock in the concepts with common dataset-creation interview questions before the graded exam.

To go deeper, the data-prep material covers transformations, aggregations, and the recipe internals in detail — but you've got the core skill. Next, rehearse it, then prove it on the graded quiz.

Discussion

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Build with a Recipe

Recreate the opportunities dataset with a CRM Analytics recipe: select objects, join Account and User with the right keys, prefix and trim columns, and add an output node — the modern, flexible way to build datasets.

Interview Questions

Rehearse common CRM Analytics dataset interview questions — what a dataset is, grain, lookups vs joins, Dataset Builder vs recipes, the sync layer, and combining datasets.