DatriseAI-first ETL

Stripe Azure Data Lake Storage

AI-first ETL from Stripe into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.

How Datrise loads Stripe into Azure Data Lake Storage

Datrise syncs Stripe's charges, customers, subscriptions, invoices, and balance transactions into Azure Data Lake Storage as partitioned Parquet in ADLS Gen2 per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes new date partitions to the container and compacts on a schedule, so re-runs update only what changed. Hive-style partitioning by load date, readable by Synapse and Databricks. ADLS hierarchical namespace makes folder layout matter, so Datrise keeps a predictable entity/date path your Azure engines mount directly.

Ideal for Azure lakehouse storage shared across Synapse and Databricks.

Endpoints

Stripe: Payments infrastructure for charges, subscriptions, and payouts.

Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.

How Stripe entities map to Azure Data Lake Storage

Stripe entityAzure Data Lake Storage objectNotes
chargesstripe_chargesid PK · custom fields → nested struct/map fields in Parquet
customersstripe_customersid PK · linked to stripe_charges
subscriptionsstripe_subscriptionsid PK · linked to stripe_charges
invoicesstripe_invoicesid PK · linked to stripe_charges

FAQ

How does Datrise handle Stripe's custom fields in Azure Data Lake Storage?

Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Azure Data Lake Storage types.

How does the Stripe to Azure Data Lake Storage sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions to the container and compacts on a schedule.

Related pipelines

Early access

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