This article covers Sheetgo (web app · app.sheetgo.com). Using the Google Sheets extension instead? See What is a Sheetgo data processor? — Automations and How to filter data in Sheetgo Automations
In the Sheetgo web app, you can combine multiple data processors in the same workflow to build more advanced logic. This helps when you need to merge sources, filter data in stages, remove duplicates, enrich records, or shape the final dataset before it reaches the destination.
How processor chains work
Processors run in sequence, and each one works on the output from the previous step. Because of that, the order matters. A filter applied before a merge or join can produce a different result than the same filter applied after it.
Processor type | What it is used for |
Filter processors | Keep only the rows or columns you need, use query logic, filter by color, or remove duplicate records. |
Transform processors | Combine datasets, enrich data, split outputs, or apply AI-based transformations. |
Tip: Put broad cleanup steps first, then use more specific logic later. This usually makes the workflow easier to test and maintain.
How to combine filters and processors in the web app
Open your workflow in Sheetgo.
Go to the processor step between the source and destination.
Add the first processor based on the result you want.
Add additional processors to build the full logic step by step.
Review the order carefully, because each processor changes the dataset passed to the next one.
Run or test the workflow and confirm the output looks correct before sharing it with other teams.
Example multi-step logic
Here are a few common ways to combine processors in the web app:
Goal | Recommended chain |
Consolidate data from several sources and keep only valid records | Merge → Filter rows → Filter columns |
Prepare a clean report from overlapping datasets | Merge → Remove duplicates → Filter rows → Data formatting |
Enrich one dataset with fields from another source, then keep only the final columns | Left-join data → Filter rows → Filter columns |
Apply advanced logic before sending the output downstream | Merge → Filter by Query → Process with AI or Split data |
Best practices
Use Filter rows when you want clear rule-based conditions.
Use Filter by Query when you need more advanced expressions.
Use Filter columns near the end when you want to shape the final layout.
Use Remove duplicates after a merge or join when the same record may appear more than once.
Use Left-join data when you need to enrich one dataset with matching values from another.
Test complex processor chains with a smaller sample first, especially when several steps depend on one another.
Warning: If you remove rows or columns too early, later processors may not have the data they need to work correctly.