DatriseAI-first ETL

Streak to Azure Data Lake Storage Integration

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

How Datrise loads Streak into Azure Data Lake Storage

Datrise syncs Streak's pipelines, boxes, stages, email threads, contacts, and tasks 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

Streak: CRM that lives inside Gmail as pipelines and boxes.

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

How Streak entities map to Azure Data Lake Storage

Streak entityAzure Data Lake Storage objectNotes
pipelinesstreak_pipelinesid PK · custom fields → nested struct/map fields in Parquet
boxesstreak_boxesid PK · linked to streak_pipelines
stagesstreak_stagesid PK · linked to streak_pipelines
email threadsstreak_email_threadsid PK · linked to streak_pipelines

FAQ

How does Datrise handle Streak'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 Streak 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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