Ever spend more time fixing a broken dataflow than actually using your data? You’re not alone. If you’re wrangling data in Domo, you’ve probably hit a few brick walls—random errors, failed outputs, or sluggish jobs that take forever. This guide is for folks who actually build and maintain Domo ETL dataflows, not just click around the dashboards. We’ll dig into the most common gotchas, what you can do about them, and how to keep your dataflows running reliably (and quickly).
Common Domo ETL Errors and What They Really Mean
Before you can fix a problem, you have to know what you’re up against. Here are the errors you’ll see most often—and what’s actually going on behind the scenes.
1. "Failed to Generate DataSet" or "Output DataSet Failed"
What it is:
Your dataflow ran, but the output step didn't work. Maybe the dataset is empty, or the transform before it blew up.
Why it happens: - You’ve got a formula error (bad calculation, divide by zero, etc.) - The input data changed—columns missing, renamed, or types shifted - The step before the output is producing no rows or unexpected results
How to fix it: - Check the run history and inspect the failing step. Domo shows you the error details—don’t just stare at the red icon. - Run the prior step and preview its output. If it’s empty, you might be filtering out everything by accident. - Look for recent changes in upstream datasets—someone else’s “quick fix” can break your stuff.
Pro tip:
If you’re using Magic ETL, the "Preview" button is your friend. Step through slowly and watch where things go sideways.
2. "DataSet Not Found" or "Input DataSet Missing"
What it is:
Your dataflow references a dataset that’s been deleted, renamed, or permissions changed.
Why it happens: - Someone deleted or replaced the source dataset - Dataset permissions changed and now you don’t have access - Dataset IDs got swapped in a rebuild or migration
How to fix it: - Go to the Data Center and search for the dataset by name or ID. - Restore or re-permission the missing dataset if possible. - If it’s gone for good, replace the input in your ETL with a valid source.
What to ignore:
Don’t waste time rebuilding the whole dataflow unless you have to. Often, it’s just a missing input you can swap back in.
3. "Job Timeout" or "Dataflow Timed Out"
What it is:
The dataflow took too long to run and got killed by Domo’s backend.
Why it happens: - Processing way more data than before (think: millions of rows) - Inefficient joins or formulas, especially cross joins or unindexed fields - Loops or bottlenecks in Magic ETL
How to fix it: - Check the row counts at every step. If a join or group by suddenly explodes the number of rows, that’s your culprit. - Simplify your logic. Do you really need that nested CASE statement? Can you aggregate earlier? - Break up monster dataflows into smaller, chained flows. Don’t try to do everything in one place.
Pro tip:
Avoid joining or grouping on free-text fields or IDs that aren’t unique. That’s a recipe for huge, slow dataflows.
4. "Memory Limit Exceeded" or "Out of Memory"
What it is:
Domo’s ETL engine hit resource limits—usually because the data got too big or a transform blew up.
Why it happens: - Loading or transforming massive datasets in one go - Using heavy transforms (like ungrouped aggregations or big joins) - Trying to pivot or unpivot huge tables
How to fix it: - Filter and aggregate as early as possible—don’t carry extra baggage through the whole flow. - Use the “Select Columns” tile to drop unused fields before heavy steps. - Split large dataflows into stages. Use intermediate outputs that you process separately.
What to ignore:
Don’t assume throwing more hardware (or yelling at IT) will fix it. Domo is a managed platform; you have to work within its limits.
5. "Invalid Formula" or "Expression Error"
What it is:
A formula tile or calculated column has a typo, a type mismatch, or uses a function that doesn’t exist.
Why it happens: - Copy-pasting SQL or Excel formulas that don’t work in Domo’s flavor of SQL or Magic ETL - Using fields that don’t exist in that step - Simple typos or missing parentheses
How to fix it: - Double-check the syntax. Domo’s formula editor will flag most issues. - Make sure all referenced fields exist at that point in the flow. - Watch out for date and string function differences between SQL and Magic ETL.
Pro tip:
When formulas get complex, break them into smaller steps. It’s easier to debug and Domo won’t choke as easily.
How to Troubleshoot Domo ETL Dataflows: Step-by-Step
If something’s broken and you don’t know where to start, use this process:
1. Check the Run History
- Open the dataflow and look at the "History" tab.
- Find the failed run and expand the error details.
- Note which step failed—don’t just restart and hope for the best.
2. Inspect Each Step’s Output
- Use the “Preview” or “Sample Data” option at each step.
- Look for sudden drops to zero rows, weird values, or fields that disappear.
- If you find a broken step, check its inputs—sometimes the upstream step is the real problem.
3. Review Recent Changes
- Did someone update an input dataset, change permissions, or tweak a formula?
- Check the Data Center for dataset changes or deletions.
- Ask your teammates if anything changed—don’t assume you’re the only one touching things.
4. Test with Smaller Data
- If the dataflow is huge, temporarily filter down to a few rows.
- This makes debugging way faster and avoids timeouts or memory errors.
- Once it works on a small set, scale back up.
5. Rebuild Broken Steps
- If a formula or join is busted, rebuild it from scratch instead of trying to fix a tangled mess.
- Use clear names for each step so you can trace what’s happening.
6. Monitor Outputs
- After fixing, run the dataflow and check the output datasets.
- Validate the data—don’t just look for a green checkmark.
Tips for Optimizing Dataflows (AKA How to Make Things Faster and More Reliable)
Getting rid of errors is step one. Making sure your dataflows don’t crawl or break again is step two.
1. Filter Early, Filter Hard
- Drop unnecessary rows and columns as soon as you can.
- The less data you carry forward, the faster (and cheaper) it’ll run.
2. Aggregate Before You Join
- Summing or grouping before joining slashes row counts.
- Joins are way faster when you’re not matching millions of records.
3. Use Intermediate Outputs
- Don’t build monster flows with dozens of steps. Instead, output key stages and chain them together.
- This makes troubleshooting and rerunning much easier.
4. Avoid Cross Joins and Cartesian Products
- If you accidentally join without a key, you’ll multiply rows fast.
- Always double-check join conditions—especially when adding new data sources.
5. Clean Up Old and Orphaned Dataflows
- Unused flows eat up resources and clutter the workspace.
- Set a quarterly calendar reminder to review and archive or delete old flows.
6. Document What Matters
- Use the description fields for steps to note quirks, workarounds, or “gotchas.”
- Your future self (or teammate) will thank you.
Honest Takes: What Works, What Doesn’t, and What to Ignore
What works:
- Previewing your data at every step. It’s tedious, but it saves hours later.
- Breaking up big flows into smaller, testable pieces.
- Being ruthless about dropping unused columns and rows.
What doesn’t:
- Hoping errors will fix themselves with a rerun (they never do).
- Building “one giant flow to rule them all”—it’ll break and be impossible to debug.
- Copy-pasting SQL from other platforms without checking Domo’s quirks.
What to ignore:
- Fancy “optimizations” unless you’re actually running into slowness or errors. Start simple.
- Panic about Domo platform bugs—they’re rare. Most issues are data or logic, not the tool itself.
Keep It Simple and Iterate
Don’t overthink it. Most Domo ETL errors come down to missing data, bad formulas, or processing too much at once. Start simple, check your work at each step, and don’t be afraid to rebuild broken parts. Dataflows are like living things—they’ll change as your business does. The best way to stay sane is to keep things tidy and fix issues as they come up, not all at once.
You don’t need to be a Domo wizard to build reliable, fast data pipelines—just methodical and a bit skeptical. Happy debugging.