Data Cloud vs a Traditional Data Warehouse

A common question when Data Cloud comes up: "We already have Snowflake (or BigQuery, Redshift, Databricks, or an on-premise SQL warehouse). Why would we need another data platform?" It is a fair question, and the honest answer is that the two are built for different jobs. They overlap at the edges, and most organisations that use Data Cloud well keep their warehouse.

What a data warehouse is built for

A warehouse is a general-purpose analytical store. Your data team designs the schema, loads data from every corner of the business and serves it to BI dashboards, finance reporting, forecasting and data science. It is excellent at history, breadth and complex SQL across large volumes.

What it is not built for is the last mile of customer engagement. Resolving many records into one person, keeping that profile current, building audiences that marketers can change themselves and pushing them into email, ads or CRM workflows all require additional tooling, such as reverse ETL services, custom identity logic and integration jobs.

What Data Cloud is built for

Data Cloud (now branded Data 360) is Salesforce's customer data platform, running on the Salesforce Platform. Data streams ingest source data into Data Lake Objects, which you map to Data Model Objects in the Customer 360 Data Model (see DLO vs DMO). Identity resolution builds unified profiles, calculated insights derive metrics, segments define audiences and activations deliver them to Marketing Cloud, advertising platforms and other targets.

Because it is part of the platform, unified profile data can also be used in Flow, on record pages and by Agentforce. That proximity to engagement and service workflows is its main advantage. It is not designed to be your finance reporting store or your data science sandbox.

Using them together: zero copy

Data Cloud supports zero copy patterns with several major warehouse platforms, including Snowflake, Databricks, Google BigQuery and Amazon Redshift. Data federation lets Data Cloud query data where it already sits instead of copying it in, and data sharing lets the warehouse read Data Cloud data, such as unified profiles, without an export job. Supported platforms, directions and features change over time, so check the current list for your org in Salesforce Help.

Zero copy does not remove design work. You still decide which tables matter, how they map to the data model, how fresh they need to be and who pays for the queries. For the broader trade-off, see data virtualisation vs replication.

Decision criteria

• What question are you answering? "What was revenue by region last quarter?" belongs in the warehouse. "Which customers should receive this message today, and in which channel?" is a Data Cloud question.
• How fresh must the data be? Near-real-time triggers and journeys favour Data Cloud. Daily or monthly analysis is comfortable in a warehouse.
• Who will operate it? Data Cloud is usually run by Salesforce architects and admins with marketing input. A warehouse is run by data engineering. Both need an owner.
• Volume and consumption. Ingesting every raw event can be costly on a consumption model. Pre-aggregate where the full grain is not needed for segmentation.
• Governance. Decide where consent and preferences are mastered, and make sure both platforms honour the same values.
• Existing investment. A mature warehouse with clean customer tables is often the best single source to feed or federate into Data Cloud.

Our view: it depends on the job

If your goal is analytics, a warehouse remains the right foundation and Data Cloud will not replace it. If your goal is acting on customer data inside Salesforce and Marketing Cloud, building that last mile on a warehouse alone means recreating much of what Data Cloud provides. For organisations doing both, the realistic answer is coexistence with a clear split of responsibilities. Our Data Cloud consulting work usually starts by drawing that line, and identity resolution is often the first design topic after it.