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Data Lake Objects (DLOs) — Data 360 reference

The raw landing layer of Data 360: what a Data Lake Object is, how Data Streams create them, and why you map them to DMOs instead of building business logic against them directly.

Reference·Last updated 2026-06-01·Drafted by Lira · Edited by German Medina

A Data Lake Object (DLO) is where ingested data lands in Data 360 (formerly Data Cloud) — the storage container in the data lake that holds a Data Stream's data in roughly the shape the source delivered it. Every Data Stream you connect creates one or more DLOs. They are the raw floor of the model: necessary, but not where business logic belongs.

What a DLO is

When a Data Stream ingests data — from a CRM connector, the Marketing Cloud connector, an S3 bucket, a web or mobile SDK, or the Ingestion API — Data 360 creates a DLO to store it. The DLO mirrors the source's structure: its fields, its types, its grain. It is storage, not meaning.

DLOs are then mapped to Data Model Objects (DMOs) — the harmonized, business-meaningful layer. DMOs are views over DLOs (see Data Model Objects). The DLO is what you ingested; the DMO is what it means.

Source (CRM Contact)  ──ingest──▶  DLO (raw landing)  ──map──▶  DMO (Individual)
      raw shape                     stored as-is               harmonized meaning

Reference:

What survives in production

Don't build business logic against DLOs

Segments and Calculated Insights should read DMOs, not DLOs. A segment built against a raw DLO inherits the source's shape and naming, so the day the source changes a column the segment breaks — and you've coupled business logic to an integration detail. Map first, then build on the DMO.

Document the source of truth per DLO

A DLO is only as trustworthy as the stream feeding it. For each DLO, write down which system is the source, how often it refreshes, and what an empty or null field means. The next person — and any agent grounded on the downstream profile — needs to know whether a blank means "unknown" or "not yet synced".

Know the DLO category

Data 360 assigns each DLO a category — Profile, Engagement, or Other — and the category constrains what the data can do downstream, including whether it participates in identity resolution and time-series insights. Choosing the wrong category at ingestion is a rework, so confirm it matches how the data will actually be used before you map.

Quick decision

Work at the DLO layer when:

  • You're debugging ingestion — confirming raw data landed as expected.
  • You're validating field types and completeness before mapping.

Work at the DMO layer (almost always) when:

  • You're building segments, Calculated Insights, or activations.
  • You're grounding an agent — it reads the harmonized profile, not the lake.

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