DatriseAI-first ETL

Sendwithus to Azure Data Lake Storage Integration

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

How Datrise loads Sendwithus into Azure Data Lake Storage

Datrise syncs Sendwithus's templates, sends, customers, delivery events, versions, and drip campaigns 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

Sendwithus: Templated transactional email and A/B testing.

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

How Sendwithus entities map to Azure Data Lake Storage

Sendwithus entityAzure Data Lake Storage objectNotes
templatessendwithus_templatesid PK · custom fields → nested struct/map fields in Parquet
sendssendwithus_sendsid PK · linked to sendwithus_templates
customerssendwithus_customersid PK · linked to sendwithus_templates
delivery eventssendwithus_delivery_eventsISO-8601 timestamp columns events

FAQ

How does Datrise handle Sendwithus'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 Sendwithus 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

Connect Sendwithus to Azure Data Lake Storage the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.