Hi All, Can someone help me understand the POV for latency being introduced when it comes to soft limits of datasets in platform. My understanding was that the limit was 20/20 regarding profile enabled datasets. So 20 event and 20 profile, but having profile enabled. When reading back through, it do...

Hi @Parvesh_Parmar Thanks for the reply. I have seen that documentation, but it does not specifically call out "Federated" Audience Composition. Just composition as a whole. Is it fair enough to consider these values as the same when it comes to guardrails? Thanks,David

Hi all-Does anyone know the limitations of Federated Audience Composition? Is it the same as the original audience composition? For example, we want to know if there is a 10 composition limit in sandbox, same as the original. @brekrut maybe you know?

Hi @BellaSnow5378 You can use the Relationship feature in AEP to connect the two schemas. Attached documentation on this below.https://experienceleague.adobe.com/en/docs/experience-platform/xdm/tutorials/relationship-ui

@GitaiBe2 @VenuMu @Indra I can confirm I do see the same within events on a profile on  my end, date is 12/31/1969. But data within the profile is correct. Must have something to do with epoch time.

I would advise to open a ticket with Support on this one. Could be defect as mentioned. Just as a thought, if you log out of Platform and re log in, does anything change?

Hi @GitaiBe2 That date usually refers to the start of epoch time, commonly seen as 1/1/1970. For your specific use case, i'm not entirely sure what could be causing it. Could you provide just a bit more context?

@brekrut got it makes sense, so only way to manage the data in the lake is essentially to delete via batch as mentioned above?

@Antony6790 might not matter but I see "messages" at the top of your payload. Can you try starting with just the following below?{"header": {

Thanks @brekrut - my understanding of the use case is to potentially highlight a retention policy within AEP data lake. The same way TTL's work for datasets, trying to understand if there is something that can be done from the data lake perspective as well.