Land source data
Preserve source keys, business time, ingestion time and schema version in an appropriately located dataset.
- Receives
- CRM, channel or product records
- Produces
- Traceable raw data
BigQuery integration.
Bring marketing and commercial datasets into a structured analytical layer with explicit source and transformation models.
Let’s connectA warehouse ingestion path with explicit delivery semantics
Choose load jobs, transfers or streaming from the freshness requirement. Streaming guarantees depend on the exact interface and stream configuration.
Control writes
For Storage Write API streaming, choose the stream type and persist offsets when the application requires the supported exactly once behavior.
- Receives
- Validated rows and stream state
- Produces
- Recorded append outcome
Model and reconcile
Build conformed facts and dimensions, then compare counts and values against source extracts.
- Receives
- Raw data and business definitions
- Produces
- Governed analytical tables
Step 1 of 3
Inspect the data contract.
Select a field to examine its owner, permitted changes and conflict handling. Clear ownership keeps updates consistent across systems.
| Field or object | Accountable owner | Mapping and control |
|---|---|---|
| Source system | Retained with tenant and object context. | |
| Ingestion application | Managed when using application created streams for the supported delivery guarantee. | |
| Data engineering | Chosen for query and lifecycle requirements. |
Resolve the important decisions.
Default stream
The default Storage Write API stream uses at least once semantics. Exactly once behavior requires the appropriate application created stream and offsets.
Location and access
Choose dataset location and permissions before moving customer data into the warehouse.
Validate before handover.
Select the scenarios to discuss with your implementation team.
0 of 3 selected
Test an example workflowPlatform documentation
A connection with a clear purpose.
Batch loads, managed transfers or streaming interfaces selected for the data freshness requirement.
We begin by identifying the business process this connection supports. Source ownership, permissions and the expected update behavior are documented before data starts moving.
The data behind the workflow.
DatasetsTablesViewsJobsTransfers
Object and field availability depend on the platform edition, API permissions and the agreed use case. Mapping defines which system owns each value and how changes are reconciled.
Decisions worth making early.
Scope & access
Define dataset region, access policies and table partitioning around the workload.
Data & behavior
Preserve source identifiers and data grain before joining campaign, contact and revenue tables.
From mapping to a maintainable connection.
Discover the workflow
Agree the trigger, source of truth, receiving system and the person accountable for exceptions.
Validate the contract
Confirm permissions, object mappings, identifiers, consent and API behavior using a representative test environment.
Test real scenarios
Exercise expected updates, duplicates, missing data, access failures and vendor rate limits.
Document and enable
Hand over field mappings, credentials ownership, monitoring guidance and recovery procedures.
The engineering detail.
Read the contracts, controls and failure behavior behind a maintainable implementation.
What could work better, together?
Tell us where your operations are today. We’ll help you see what comes next.