Comparison of Snowflake vs Salesforce Data Cloud for enterprise analytics

The comparison people search for is a winner. Snowflake, or Data Cloud, pick one, retire the other, and stop paying twice. The search is already wrong about the name, and it is wrong about the prize. There is no benchmark on this page. A borrowed speed or a borrowed price would be a lie about your volumes and your contract. If speed or cost is the argument, measure the path you intend to run, and read your own agreement. Until then, compare responsibilities. Responsibilities are knowable. A bake-off you have not run is not.

#They answer different questions

The Salesforce product in this comparison is Data 360. Data Cloud was rebranded to Data 360 on 14 October 2025. The behaviour is unchanged. You may still see the old name in the application and in the documentation. "While the name is new, the functionality and content remains unchanged." The rename does not make Data 360 a warehouse, and it does not make a warehouse a system of engagement.

Snowflake, or any warehouse you already run, is where large history is modelled, retained, and shared with people who think in tables. Data 360 is where a subset of that work has to line up with a CRM person, a CRM account, a segment, and an agent. Substituting one for the other throws away the job the other one does. A warehouse that cannot hold CRM identity is being asked to be a profile. A Data 360 org that is asked to store five years of finance grain is being asked to be a warehouse, with a second copy of governance you already operate.

Do not run this as a winner-take-all business case. The honest design, for a serious estate, often keeps both. The reason is not enthusiasm for platforms. The reason is that the jobs do not overlap as much as the diagram suggests.

#What to leave in the warehouse

Leave history that has no waiter in the CRM. Years of transactions. Detailed events. Finance grain. Data a data team already governs, with tests and retention they already run. Model it there. Copying that history into Data 360 so that "everything is in Salesforce" duplicates governance and creates a second bill for the same rows. Retention on the warehouse does not retention the copy. A correction in the warehouse does not repair the copy until a load exists. Access on the warehouse does not answer who can see the copy.

Ingest into Data 360 when a CRM process or a segment must hold the rows, or when a person is waiting on a narrow event. The help page is explicit that you ingest streaming and batch data. Use batch for the history you do copy. Use a stream only for the event with a named waiter. A full replica of the warehouse on a stream is how this comparison gets expensive without getting clearer. Each streamed row is processing. History nobody is waiting on does not deserve that meter.

The attributes worth copying are the ones a segment, a profile, or an agent will actually filter on. Email, where the CRM is the authority. Consent, with a source and a time. The CRM account id. A narrow slice of revenue if a segment truly filters on it and you have accepted a refresh. Leave the rest. A column with no consumer is not a gap in the platform. It is a column that should stay where the analysts already trust the grain.

#What Data 360 is for

Identity, segments, and activation sit with the CRM, not in a worksheet on the warehouse. Identity resolution generates unified profiles from source profile data stored in Data 360. Unified profiles are keys back to source records. They are not a golden record. The engine does not pick a winning value and does not override source values. The warehouse does not become a system of engagement because you drew a line to it, and Data 360 does not become a survivor record because you unified. If the warehouse and the CRM disagree about an email, the profile will point at both until you pick an authority. Two authorities means a reconciliation project. That project is the failure this comparison is supposed to prevent.

Agents make the split sharper. The help page says to ground your data for Agentforce. An agent answering in the CRM flow needs grounded facts tied to the person or the account in front of it. That is a Data 360 problem: a small set of facts you will defend, at a grain the conversation uses. An analyst studying a long run of margin is a warehouse problem, at a grain the conversation must not use. Feeding the agent the entire warehouse schema, because a connection makes it possible, is how you get confident answers from the wrong grain. The agent will sound as if it read the finance system. It read a join you did not constrain.

Put the attributes a segment will filter on into the Data 360 model, with the sentence of the filter written down. Leave exploratory questions in the warehouse, where an analyst can be wrong in a workbook without activating a person. Activation is the line. Once a column can put someone into a send, it needs consent, an owner, and a grain. The warehouse's permission model does not supply those by being connected.

#Zero copy is the reason to keep both

Zero copy is federation. The help page says: "Connect data using zero copy data federation." The warehouse stays the store. Data 360 can query a governed slice without a second copy of the bytes. That is the point of keeping both. It is not a latency promise. A warehouse that is fine for an analyst at nine in the morning may not be fine as a live dependency while an agent is mid-sentence or a segment refresh is due. Measure the query you intend, at the hour you intend, before you promise the path to a person who is waiting. Until that measurement exists, say you have not measured it.

Someone has to own the query load you are about to add. The warehouse already has jobs. A segment refresh that scans a wide table during those jobs will be felt by the analysts, and an agent read during the same window will be felt by the customer. Name the person who can say no to a heavier query. Federation did not hire that person for you.

Federation still has to be modelled. Which tables. Which keys. Which columns a profile or a segment is allowed to use. A federated schema with every column visible is not a model. It is a window, and windows get used by the next person who needs a filter in a hurry. Write an allow-list. Reject columns with no authority and no consumer.

Decide attribute by attribute, not platform by platform. Revenue history: warehouse, federated only if a segment truly filters on a defined slice, otherwise left alone. Email, consent, CRM account id: Data 360, because that is where identity and activation live. If both sides claim the same email, pick one owner and say what happens when that owner is empty. Last-write-wins will hand the attribute to whichever pipeline ran last, including a normalisation job. That is a default, not a policy.

The same pattern, when the external store is a lake rather than the warehouse you already govern, is ingesting or federating an external lake. The jobs do not change because the vendor name on the store changes. This page still will not name a connector. Confirm any named connector in the current org before it enters a plan.

Tableau Next does not replace Tableau. Analysts who already work in Tableau on the warehouse should keep that habit for warehouse questions. The help page lets you expand Data 360 using Tableau or Tableau Next. Moving the warehouse workbook onto a new surface does not make it a CRM profile, and it does not retire Tableau. A chart of federated finance grain, titled as if it were the unified customer, is the double story this comparison exists to stop. Put the grain in the title. Person, account, or ledger. If the title cannot say which, the chart is not ready.

#A page, not a matrix

Write the decision on one page. Each subject. The store. The pattern, federate or ingest. The key. The consumer: segment, agent, or analyst. If a row has no consumer, it does not need a Data 360 path. That page is the comparison. A feature matrix is not. Matrices award a winner. You are not awarding one.

Read the pattern column with the consumer in the room. Federation is attractive because it feels like you avoided a decision. You avoided a copy. You did not avoid the model, the key, the allow-list, or the question of what the agent does when the warehouse does not answer. For most service conversations, a slightly stale narrow copy is a better failure mode than a live read that fails. Settle that per object. A single policy of "always federate" or "always copy" is how the page collapses back into a slogan.

#What gets expensive to reverse

Cheap: adding a column to a federated allow-list that no segment uses yet, or adding a batch attribute nobody has activated. Do these without a steering pack.

Expensive: copying a wide history you later wish you had left in the warehouse, once a segment's response time depends on the copy. Expensive in the other direction: pointing an agent at a federated grain, once sellers have repeated the answers. Changing the source of truth for an attribute you have written back into the CRM. Loosening identity rules because the warehouse brought a new population of shared keys. A new source is a change to identity resolution, not a simple addition. Re-test the pairs that must not match before it goes live.

The profile will not clean that new population for you. Blank keys stay blank. Conflicting emails stay conflicting. If the warehouse is dirtier than the CRM, federating it into a profile imports the dirt into the conversation. Count blanks and conflicts before you connect, the same as you would before a CRM ingest. The engine unifies what you give it.

The pre-ingestion decisions that sit under this page, scope of unification, match rules, attribute ownership, copy versus reference, and a named owner, are in getting Data 360 right the first time. Start there if the comparison is still being framed as a replacement. It is not a replacement. It is a split of jobs, and the split is the design.