Move Stripe customer data into BigQuery every weekday before 8am.
Stripe
Map customer sources. Connect customer Snowflake, Databricks, BigQuery, S3, CSV, warehouse, and file sources.
Customer data onboarding and schema mapping
Customer onboarding requires custom schema mapping, PII detection, chunking, validation, and delivery that generic connectors rarely handle cleanly.
Mage
I mapped this marketing use case as a Mage workflow.
- profile Snowflake, Databricks, BigQuery, S3, and CSV sources
- detect PII, map schemas, and validate customer-specific quality rules
- prepare chunked exports, APIs, queues, and storage handoffs
- register schema mappings and approvals for reuse
You can inspect the source profile, PII tags, semantic mappings, quality checks, chunks, delivery state, and onboarding template before the platform uses the data.
Use Mage to onboard customer data with reusable schema mapping and delivery workflows.
Customer onboarding requires custom schema mapping, PII detection, chunking, validation, and delivery that generic connectors rarely handle cleanly.
Customer Snowflake, Databricks, BigQuery, S3, and CSV sources flow into Mage profiling, PII detection, semantic mapping, quality checks, chunked exports, and platform API/message-queue delivery.
From marketing source chaos to governed action
Use Mage as the execution layer between the specific sources, validation rules, governed delivery targets, and reusable context this marketing workflow needs.
Why did revenue freshness drop?
The Stripe ingest arrived 42 minutes late, delaying the customer revenue model.
Stripe ingest delayed 42 min
Validate onboarding quality. Run profiling, PII detection, schema validation, semantic checks, chunk checks, and delivery readiness checks.
Customer syncRun delayed
Inspect run
Late source foundRetry availableOpen logs
Register reusable mappings. Version mappings, PII tags, quality checks, template variables, approvals, and source profiles for future onboardings.
Pipeline
Table
Chart
File
AnswerRevenue freshness dropped after the Stripe ingest arrived late.
Deliver platform-ready data. Publish chunked exports, platform API payloads, queue events, storage handoffs, and mapping context.
How Mage runs customer data onboarding
Mage turns customer data onboarding into a repeatable workflow for profiling, mapping, validation, and delivery.
Onboard this customer's data. Profile their warehouse and file sources, detect PII, map schemas semantically, validate quality, chunk exports, and deliver to our platform API.
The customer onboarding workflow is ready to review. I profiled sources, tagged PII, mapped fields, checked quality, chunked exports, and prepared platform delivery.
Customer activity
3open support tickets34 daysto renewal
Start with customer data. Start with the new customer source, schema, file drop, warehouse, or platform delivery target that needs custom onboarding.
Map customer sources. Connect Snowflake, Databricks, BigQuery, S3, CSV, and warehouse sources without hand-building every pipeline.
Validate schema mapping. Run profiling, PII detection, schema validation, semantic mapping checks, and delivery readiness checks.
Create reusable templates. Turn field mapping, schema interpretation, PII rules, and customer-specific logic into reusable blocks and templates.
Send platform-ready data. Deliver chunked exports, platform API payloads, message-queue events, storage outputs, and mapping context.
Register onboarding context. Keep customer-specific mappings, PII tags, quality evidence, approvals, and template variables reusable.
What the workflow includes
Profile customer sources Mage profiles customer Snowflake, Databricks, BigQuery, S3, and CSV sources so schema drift and missing fields are visible before onboarding continues.
Map schemas safely PII detection, semantic mapping, quality checks, and approval steps make customer-specific data safe to normalize and deliver.
Deliver reusable mappings Chunked exports, platform APIs, message queues, storage handoffs, and mapping context stay connected to the same workflow history.
Use case details
The workflow view shows sources on the left, Mage execution in the middle, and governed outputs plus AI context on the right.
Business problem
Customer onboarding requires custom schema mapping, PII detection, chunking, validation, and delivery that generic connectors rarely handle cleanly.
Mage workflow story
Profile customer sources, detect PII, map schemas, validate quality, chunk exports, and deliver platform-ready data through governed workflows.
Workflow diagram
Customer Snowflake, Databricks, BigQuery, S3, and CSV sources flow into Mage profiling, PII detection, semantic mapping, quality checks, chunked exports, and platform API/message-queue delivery.
Customer sources
SnowflakeDatabricksBigQueryS3 + CSV
Source fieldMage checkCanonical fieldemailPII detectedcustomer.emailcust_idsemantic mapidentity_idorder_totalquality passrevenue.amount
Chunked exportReady
Platform APIQueue
Bring Mage the customer warehouses, files, mappings, and delivery targets behind onboarding. Mage will map the profiling, PII detection, semantic mapping, quality checks, chunking, and platform delivery flow.