DatriseAI-first ETL

Marketo Bulk Azure Data Lake Storage

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

How Datrise loads Marketo Bulk into Azure Data Lake Storage

Datrise syncs Marketo Bulk's records, events, and configuration objects 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

Marketo Bulk: SaaS or API data source for analytics and warehouse sync.

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

How Marketo Bulk entities map to Azure Data Lake Storage

Marketo Bulk entityAzure Data Lake Storage objectNotes
recordsmarketo_bulk_recordsid PK · custom fields → nested struct/map fields in Parquet
eventsmarketo_bulk_eventsISO-8601 timestamp columns events
configuration objectsmarketo_bulk_configuration_objectsid PK · linked to marketo_bulk_records

FAQ

How does Datrise handle Marketo Bulk'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 Marketo Bulk 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

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