sf-datacloud-prepare

--phase prepare --json 2. Inspect existing ingestion assets sf data360 data-stream list -o < org

2

/dev/null sf data360 dlo list -o < org

2

/dev/null 3. Confirm the stream category before creation Use these rules when suggesting categories: Category Use for Typical requirement Profile person/entity records primary key Engagement time-based events or interactions primary key + event time field Other reference/configuration/supporting datasets primary key When the source is ambiguous, ask the user explicitly whether the dataset should be treated as Profile , Engagement , or Other . 4. Create or inspect streams intentionally sf data360 data-stream get -o < org

--name < stream

2

/dev/null sf data360 data-stream create-from-object -o < org

--object Contact --connection SalesforceDotCom_Home 2

/dev/null sf data360 data-stream create -o < org

-f stream.json 2

/dev/null sf data360 data-stream run -o < org

--name < stream

2

/dev/null 5. Check DLO shape sf data360 dlo get -o < org

--name Contact_Home__dll 2

/dev/null 6. Choose the right refresh mechanism Use the smaller refresh scope that matches the user goal: sf data360 data-stream run -o < org

--name < stream

2

/dev/null sf data360 connection run-existing -o < org

--name < connection-id

2

/dev/null data-stream run is the closest match to a stream-level refresh or re-scan. connection run-existing runs at the connection level and can be useful for some connector workflows, but it is not a reliable replacement for stream refresh on unstructured sources. For unstructured document connectors, prefer data-stream run when the goal is to re-scan newly added or changed files. 7. Handle unstructured sources deliberately For SharePoint-style document ingestion, a minimal unstructured DLO payload can look like: { "name" : "my_udlo" , "label" : "My UDLO" , "category" : "Directory_Table" , "dataSource" : { "sourceType" : "SF_DRIVE" , "directoryAndFilesDetails" : [ { "dirName" : "SPUnstructuredDocument//" , "fileName" : "*" } ] , "sourceConfig" : { "reservedPrefix" : "$dcf_content$" } } } Use the UI for the first-time unstructured setup when the user needs the richer end-to-end pipeline. The UI path can seed additional document metadata fields and downstream assets that a bare CLI DLO create flow may not provision automatically. 8. Use the local Ingestion API example for send-data workflows For external systems pushing records into Data Cloud: create the connector in sf-datacloud-connect upload the schema with sf data360 connection schema-upsert create the stream in the UI when required send records with the local example in examples/ingestion-api/ cd examples/ingestion-api cp .env.example .env python3 send-data.py Key details: auth is a staged flow: JWT → Salesforce token → Data Cloud token the ingestion endpoint uses the tenant URL, not the Salesforce instance URL 202 means the payload was accepted for processing, not that records are queryable immediately validation failures often surface in the Problem Records DLO family 9. Only then move into harmonization Once the stream and DLO are healthy, hand off to sf-datacloud-harmonize . High-Signal Gotchas CRM-backed stream behavior is not the same as fully custom connector-framework ingestion. sf data360 data-stream run and sf data360 connection run-existing are not interchangeable; prefer stream-level refresh for unstructured rescans. SFDC streams sync on a platform-managed schedule; data-stream run is not the general control path for CRM connector refresh. Some external database connectors can be created via API while stream creation still requires UI flow or org-specific browser automation. Do not promise a pure CLI stream-creation path for every connector type. Initial SharePoint-style unstructured setup can be richer in the UI than in a minimal CLI DLO create flow. Stream deletion can also delete the associated DLO unless the delete mode says otherwise. DLO field naming differs from CRM field naming, including __c → _c transformations. Query DLO record counts with Data Cloud SQL instead of assuming list output is sufficient. CdpDataStreams means the stream module is gated for the current org/user; guide the user to provisioning/permissions review instead of retrying blindly. Output Format Prepare task: Source: Target org: Artifacts: Verification: Next step: References README.md examples/ingestion-api/README.md ../sf-datacloud/assets/definitions/data-stream.template.json ../sf-datacloud/references/plugin-setup.md ../sf-datacloud/references/feature-readiness.md