DatriseAI-first ETL

Little Green Light Azure Data Lake Storage

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

How Datrise loads Little Green Light into Azure Data Lake Storage

Datrise syncs Little Green Light's contacts, accounts, deals, activities, and lifecycle events 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

Little Green Light: Nonprofit CRM for donors, campaigns, and stewardship.

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

How Little Green Light entities map to Azure Data Lake Storage

Little Green Light entityAzure Data Lake Storage objectNotes
contactslittle_green_light_contactsid PK · custom fields → nested struct/map fields in Parquet
accountslittle_green_light_accountsid PK · linked to little_green_light_contacts
dealslittle_green_light_dealsid PK · linked to little_green_light_contacts
activitieslittle_green_light_activitiesISO-8601 timestamp columns events

FAQ

How does Datrise handle Little Green Light'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 Little Green Light 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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