Hey everyone,

I’m working on an Alteryx workflow where I need to join two datasets using a common Customer ID field. However, after joining them, the output contains significantly more records than my original dataset.

I’ve checked the data types and confirmed that both fields use the same format, but I’m still getting unexpected results.

Could this be happening because of duplicate keys in one or both datasets? What’s the best way to identify and resolve these duplicates without losing important records?

Any suggestions or approaches would be really helpful. Thanks!

Hey everyone,

I’m working on an Alteryx workflow that processes a large dataset with several joins, filters, and formula tools. The workflow works correctly with smaller datasets, but when I run it on the full dataset containing over a million records, it takes significantly longer than expected.

I’ve tried removing a few unnecessary tools, but the performance improvement has been minimal.

Are there any best practices for optimizing Alteryx workflows when working with large datasets? Would using In-DB tools, reducing unnecessary joins, or changing the workflow structure help?

I’d appreciate any suggestions from people who’ve faced a similar issue!

A common challenge in analytics is dealing with inconsistent formats, duplicate records, missing values, and mismatched definitions across systems. Transformation tools can standardize much of this, but repeated fixes can turn into a growing collection of exceptions and workarounds.
At some point, it becomes worth asking whether the pipeline is solving the problem or simply compensating for it.
For those working with ETL, Alteryx, SQL, or Python pipelines, what tells you that a transformation belongs in the pipeline versus being a problem that should be fixed in the source system?
How do you handle it when the source team cannot make changes?

A common challenge in analytics is dealing with inconsistent formats, duplicate records, missing values, and mismatched definitions across systems. Transformation tools can standardize much of this, but repeated fixes can turn into a growing collection of exceptions and workarounds.
At some point, it becomes worth asking whether the pipeline is solving the problem or simply compensating for it.
For those working with ETL, Alteryx, SQL, or Python pipelines, what tells you that a transformation belongs in the pipeline versus being a problem that should be fixed in the source system?
How do you handle it when the source team cannot make changes?

I’m trying to fetch data from the Facebook Ads Insights API, but every request returns the following error:

{
  "error": {
    "message": "(#3) Application does not have the capability to make this API call",
    "type": "OAuthException",
    "code": 3
  }
}

The app has a valid access token, and I can successfully make requests to other Facebook Graph API endpoints.

Example request:

GET https://graph.facebook.com/v2.8/act_<AD_ACCOUNT_ID>/insights
  ?fields=impressions,clicks,spend
  &access_token=<ACCESS_TOKEN>

Has anyone encountered this issue with the Insights API specifically?

I’m wondering if this is related to:

• Missing app permissions
• App review requirements
• Ads API access approval
• Business Manager configuration

What was the actual fix in your case?